CHAPTER ONE

INTRODUCTION

1.1Background of the Study

In the world today, exchange rate is a major determinant of international world trade among various economies and nations, the role of which can never be overemphasized for the growth of any economy. This is because international trade provides opportunities to expand both the production possibilities as well as the consumption basket available to a nation (Adewuyi, 2005). Exchange rate as defined by the Business dictionary is the price of a nation’s currency that can be exchanged for a foreign or another country’s currency. Exchange rate has also been defined as the rate at which one currency exchanges for another (Jhingan, 2005). In other words, a country’s exchange rate is an important determinant of the growth of its cross-border trading and it serves as a measure of its international competitiveness (Bah and Amusa, 2003).

In light of the above, Edwards (1994) stated that it is not an overstatement to say that exchange rate behavior now occupies a central role in policy evaluation and design. Exchange rate is a vital macroeconomic variable in economic policy making and as such its fluctuations leads to adverse changes in all other macroeconomic variables. Mordi (2006) also argued that the exchange rate movements have effects on inflation, price incentives, fiscal viability, competitiveness of export, efficiency in resources allocations, international confidence as well as balance of payment equilibrium. Exchange rate is one of the key barometers of the economic performance of a nation indicating growth in terms of output, demand conditions, the levels and trends in monetary and fiscal stance (Afolabi, 1991). The role and effect of exchange rate on macroeconomic performance has continued to generate a lot of interest among economic scholars. Many economists argue that exchange rate stability facilitates production activities and economic growth, they are also of the view that misalignment in real exchange rate could distort production activities and consequently hinder export growth and generate macroeconomic instability (Mamta Chowdhury, 1991). Exchange rate policies guide investors on the best way they can strike a balance between their trading partners and investing at home or abroad (Balogun, 2007).

Agu, 2002 stated in his work that the main aim of an optimal exchange rate policy is to assist in attaining Real Exchange Rate in order to maintain both the internal and external balance of a nation. Solely or in combination with Real exchange rate, the Nominal exchange stands as a vital policy tool in every economy, this is due to the fact that it influences to a great deal the resource allocation as well as international and structural changes in every economy. The concept of Real Exchange Rate (RER) stems from the awareness that changes in prices as well as inflation is what results in Nominal exchange rate movements. Over the years, the federal government of Nigeria has implemented various exchange rate policies in their bid to promote the nation’s trade and economic growth (Adewuyi, 2005). Before the year 1986, the Nigerian Government was functioning under a fixed exchange rate regime in which the British Pound was the key determinant of the Nigerian Naira exchange rate which was subsequently substituted with the American Dollar.The fixed exchange rate system was implemented and maintained from 1960 up until the collapse of the Bretton Woods System in the early 1970s. With the end of the Bretton Woods System, there followed a strict adoption of the floating exchange rate regime in Nigeria. Under this regime, the Nigerian Naira (Exchange Rate) was allowed to float and its value with respect to the value of an American Dollar was determined solely by the market forces of demand and supply.After this period, the pegged arrangement was adopted and subsequently the various free floating regimes in 1986 following the implementation of the Structural Adjustment Programme (SAP) (Sanusi, 2004). Some of the other exchange rate policies implemented by the Nigerian Government include; the Dutch Auction System (DAS), the Interbank Foreign Exchange Market (IFEM), the Second-Tier Foreign Exchange Market (SFEM), the Autonomous Foreign Exchange Market (AFEM), the Enlarged Foreign Exchange Market (FEM), Wholesale Dutch Auction System (WDAS), to mention but a few.

According to Organization of Petroleum Exporting Countries (OPEC), Nigeria is the second largest Oil Producer and Exporter in the world today and as such depends greatly on its crude oil export earningswhich makes up about 95% of the country’s foreign exchange and export earnings and about 80% of government revenue and its annual budget. (EIA, 2010). In light of this, such countries as Nigeria are bound to experience an appreciation in exchange rate when oil prices rise and a depreciation when it falls. For instance, from the periods of 1980 to 1985, after an increase in oil prices Nigeria experienced an upward trend in its exchange rate which in turn led to the loss of competitiveness for the Nigerian Economy, subsequently in 1986 the Nigerian Economy experienced an acute depreciation in its exchange rate which was as a result of decrease in oil prices and which in turn led to the adoption of the Structural Adjustment Programme (SAP) and eventually to the devaluation of the Nigerian Naira as a last resort to make the Nigerian Exchange Rate favourable for the economy.

Although there have been several exchange rate regimes implemented by various governments, the extent to which these reforms and policies have positively affected the Nigerian Exchange Rate has remained unascertained. Since the implementation of the Structural Adjustment Programme (SAP) in 1986, with one of its objectives being to restructure the production base of the nation in favor of an increased production of agricultural export goods, the Nigerian Naira Exchange Rate has barely experienced some level of stability and has continued to depreciate thus marring the economic performance of the country.The problems caused by the combination effect of high and increasing oil prices and exchange rate fluctuations on the economic stability and growth of an oil producing nation such as Nigeria has been extremely felt. As a result of this,Exchange Rate Fluctuations and its effect on price and output has attracted the attention of many studies. Several explanations and theories have been developed by economic scholars for the lead factors that cause exchange rate fluctuations and hinder the beneficial outcomes of efficient macroeconomic policies and strategies in determining exchange rate.

The effects of the changes in nominal and real exchange rates among all other macroeconomic variables on macroeconomic conditions has become crucial because of financial markets integrations and capital flows accelerations. In international trade, there are mainly two sources of risks and these include; fluctuations in world prices and exchange rate instabilities as such studying the behavior of the exchange rate as well as its key determinants is vital for several reasons. One of these reasons is that the relationship between a nation’s rate of exchange and its economic growth attained through international trade is very important in policy decision making. This research aims to study the key determinants of the exchange rate in the Nigerian Economy in an attempt to understand and eliminate the causes of these instabilities.

1.2Statement of the Problem

Foreign Exchange Rate has been established as an inevitable ingredient for the economic growth and development of every developing nation. Foreign Exchange Policies have a great impact on the economic activities of a nation and a greater effect on the direction of all of its other macroeconomic variables. The mechanisms of exchange rate determination varies among systems, countries and economies and this must be effectively carried out in a manner that would bring about the efficient allocation of scarce resources so as to achieve sustainable growth and development, (Udoye, 2009). Jhingan, 2005 opined that to maintain both internal and external balance, a country must control its exchange rate.

It has also been made clear that when an optimal exchange rate is combined with other macroeconomic variables enables an economy to attain its macroeconomic goals of full employment, sustainable economic growth, increased standard of living, price stability, equilibrium balance of payment, etc. On the other hand with a least optimal exchange rate these macroeconomic goals would be unattainable. There have been various types of exchange rate regimes practices in countries all over the world, with nations adopting regimes ranging from fixed exchange rate regimes to free floating regime or a combination of other various types of exchange rate regimes that include but are not limited to the Adjustable Peg Exchange Rate Regime, Target Zone Exchange Rate Regime, the Crawling Peg Exchange Rate Regime, the Managed Float Exchange Rate Regime etc. all in line with a nation’s economic requirements and needs, (Udoye, 2009). Over the years this has been synonymous with Nigeria as exchange rate regimes have moved from the Fixed Exchange Rate Regime to the Pegged Exchange Rate Regime and subsequently to the varying types of Floating Exchange Rate Regimes, (Sanusi, 2004). But still the act of determining an economically appropriate and tenable exchange rate in Nigeria has been a difficult one as it is the goal of every economy to have a sustainable and stable exchange rate. One of the main characteristic of the fixed exchange rate regime in Nigeria was that it induced the overvaluation of the Nigerian Naira and was further propelled by exchange control regulations that caused a large degree of distortions in the Nigerian Economy. Subsequently this caused an increase in the rate of importation with adverse ramifications on local productions, external reserves as well as Balance of Payment positions. Also the period of fixed exchange rate was plagued by extreme practices committed by end users and foreign exchange dealers. These and many more problems informed the adoption of a more flexible exchange rate regime in the context of the Structural Adjustment Programme (SAP) adoption in 1986, (Sanusi, 1988).

The attempt to attain an optimal and stable exchange rate led to the implementation of the purchasing power parity model by the Central Bank of Nigeria, (CBN). This model was set as an avenue to gauge nominal exchange rate movements while determining deviations from the equilibrium exchange rate. Although the model which functions basically as a relative price, does not give a concise criterion for choosing a base period and has been noted for its insensitivity to short run policy decisions, it however gives a reasonable framework for a comparative analysis of the performance of trading partners.

An optimal Real exchange rate causes international competition among the local producers of tradable goods and services and also prevents imports from being artificially cheaper than their comparable domestic alternatives. Exporters are also not disadvantaged by the exchange rate when the Real exchange rate is right, (Maciejewski, 1983), however, in Nigeria this has not been so. According to Williamson (1994), a country’s optimal real exchange rate is determined by some key macroeconomic variables and that the long-run value of the optimal real exchange rate is determined by suitable permanent values of these macroeconomic variables. The actual factors that determine the exchange rate in an economy such as the Nigerian economy (i.e. an open economy) is one of the most prolonged economic problems in global economics. According to Amatya et al, 2004, the single most influential idea in this context has been the Mundellian prescription that if shocks facing a country are mainly monetary in nature then fixed exchange rates are optimal whereas flexible rates are optimal if the shocks are mostly real. The main criticism of Mundell’s findings was the assumption of sticky prices in the goods market, (Udoye, 2009). Subsequent to the collapse of the Bretton-Woods regime and with the implementation of the floating exchange regime, exchange rates in Nigeria have over time become highly unstable and with no substantial cause-effect relationship to changes in other macroeconomic variables and as such the Nigerian exchange rate has been a constant topic of discussion among scholars, policy decision makers and relevant authorities in both the monetary and development sectors.

With the historical proof of the various exchange rate regimes that have been implemented in Nigeria over the years, it shows that the federal authorities have invested a lot in achieving the optimal exchange rate regime. Sanusi 2004 informed the importance of maintaining a realistic exchange rate for the Nigerian Naira and also the need to minimize distortions in production and consumption, increase the inflow of non-oil export receipts and attract foreign direct investment in order to prevent the overvaluation of the Nigerian Naira while ensuring that the competitiveness of the external sector remains intact. In the 1960’s and early 1970’s Nigeria operated a fixed exchange rate, this was replaced with the pegged exchange rate system and subsequently shifted to the various types of exchange rate regimes since the introduction of the Structural Adjustment Programme from 1986 till present. (Sanusi, 2004)

With the adoption of all these varying exchange rate regimes over the years, the Nigerian economy has still been unable to maintain a balance internally as well as externally thus leading to the problems this study seeks to solve.

1.3Aims and Objectives of the Study

The main objective of this study will be to examine the determinants of exchange rate in the Nigerian economy.

The specific objectives of this study would be;

to examine the trends in Real Exchange Rate (RER), Gross Domestic Product (GDP), Inflation (INF) and Trade Openness (TOP) in the Nigerian economy from 1984 – 2016.

to ascertain the relationship between Exchange Rate and GDP in Nigeria.

to examine the relationship between Exchange Rate and Inflation in Nigeria.

to investigate the relationship between Exchange Rate and Trade Openness in Nigeria.

to make appropriate recommendations on the key determinants of Exchange Rate based on the findings of this study.

1.4Research Questions

In view of the above highlighted problem, the following research questions have been raised to guide this study;

What are the determinants of exchange rate in Nigeria?

What are the trends in Real Exchange Rate (RER), Gross Domestic Product (GDP), Inflation (INF) and Trade Openness (TOP) in the Nigerian economy from 1984 – 2016?

What is the relationship between exchange rate and GDP in Nigeria?

What is the relationship between exchange rate and Inflation in Nigeria?

What is the relationship between exchange rate and Trade Openness in Nigeria?

1.5Research Hypothesis

In accordance with the research questions, the following null hypotheses have been developed for testing in this study;

H2: There is no significant relationship between exchange rate and GDP in Nigeria?

H3: There is no significant relationship between exchange rate and Inflation in Nigeria?

H4: There is no significant relationship between exchange rate and Trade Openness in Nigeria?

1.6Scope of the Study

This study would be limited to a time period of 1984 to 2016 giving this research a total period of 32 years. The reason for the selection of the above mentioned period was made on the bases of ensuring sufficient data availability and also as a result of the various exchange rate regimes that have been implemented in Nigeria over these years. The intended scope of this is not too large but large enough to ensure that data meets the criteria for time series analysis while ensuring that every element involved in the accuracy of the outcome of this research is duly investigated.

1.7Significance of the Study

The purpose of this study is to identify the main determinants of exchange rate in Nigeria. One objective of the National Economic Empowerment and Development Strategy (NEEDS) Policy Thrust Document is to determine the Nominal exchange rate using the Retail Dutch System (DAS) and adopt the Wholesale Dutch Auction System (WDAS) in the medium to long term, (NEEDS, 2004). This objective concurs with the ultimate goal of the monetary arm of the federal government of Nigeria which is to ensure stability in prices of goods and services as well as exchange rate. In view of the above, the economic significance of identifying the determinants of exchange rate cannot be overstated, and as such this research would be of relevance to the Nigerian Economy in the following ways; Firstly, the findings of this research will serve as a future guideline to the monetary policy decision makers in terms of ensuring the formulation of better and more appropriate financial sector reforms in their bid to resuscitate the Nigerian economy and its international advantage by maintaining an optimal exchange rate. Secondly, this research would be immensely useful to the overall Nigerian economy as it would attempt to provide various measures of ensuring increased exportation, decreased importation, efficient resource allocation, attainment of full employment and increased investments both in the private and foreign sectors all via optimal and stable exchange rate systems/regimes. Lastly, the findings of this research study would seek to contribute more information to the already existing knowledge on this subject matter and in turn assist economic planners in necessary developmental decisions for the sustainable growth of the economy.

1.8Organization of the Study

This research work would comprise of 5 chapters. The chapter one – Introduction is the introductory aspect of this research that seeks to give background knowledge of the past and present state of the subject matter. The Chapter two – Literature review attempts to investigate the findings of all other previous studies that have been carried out on the subject matter in order to see if there are certain existing gaps in the knowledge of the research topic. The chapter three – Methodology attempts to suggest the types and sources of data that would be used in this research study and also how these data would be analyzed using varying econometric and statistical tools. The chapter four – Discussion of results seeks to explain in the details the findings from the data obtained and analyzed while showing the existing economic relationships between the variables involved in this research. The chapter five – Summary, Conclusion and Recommendations would give a summary of all findings of this research and would then attempt to make appropriate recommendations based on these findings.

CHAPTER TWO

LITERATURE REVIEW

This chapter provides an overview of previous research works that have been carried out on this topic. It intends to introduce the framework for the subject matter. This chapter will focus on establishing all previous works carried out on exchange rate determinants in Nigeria, the findings and conclusions and would act as a base for the work that is to be carried out in this research. In this chapter all conceptual clarifications and definition of terms would be made, it would also attempt to expose the various types of exchange rate, exchange rate determination models as well as reviewing both theoretical and empirical literature on exchange rate determination in Nigeria.

2.1Conceptual Clarifications

In conceptual terms, an exchange rate refers to the price of one nation’s currency expressed in terms of another nation’s currency. Exchange rate can also be said to be the ratio between a unit of one currency and the amount of another currency for which that unit can be exchanged at a particular time (Ngerebo-a andIbe, 2013). Also Exchange rate as defined by Mordi, (2006) is the price of one currency vis-à-vis another and is the number of units of a currency required to buy another currency. The Exchange rate of one particular currency shows the connection between local and foreign prices of goods and services of an economy. Exchange rate also can either rise or fall. A rise in exchange rate can occur when lesser units/values of a nation’s local currency exchanges for a unit of a foreign currency while a fall in exchange rate can occur if a greater unit/value of a nation’s local currency exchanges for a unit of a foreign currency.

2.1.1Types of Exchange Rates

In line with Economic history, there exists two main concepts of exchange rate; the real exchange rate and the nominal exchange rate. The nominal exchange rate (NER) is a monetary concept which measures the relative price of two countries’ moneys or currencies, e.g., naira in relation to the U.S. dollar (e.g., #198.00: US$ 1.00) and vice versawhile the real exchange rate (RER), as the name implies, is a real concept that measures the relative price of two goods-tradable goods (exports and imports) in relation to non-tradable goods (goods and services produced and consumed locally) (Obadan, 2006). Also, the nominal exchange rate is the number of unit of domestic currency that must be given up to get a unit of foreign currency, in other word, nominal exchange rate is the price of domestic currency in term of foreign currency while The real exchange rate is the relative price of foreign goods in term of domestic goods, in other word, it is the exchange rate adjusted for price.

More complex measurements of exchange rates go beyond bilateral comparisons to include multilateral comparisons and a good example in this direction is real effective exchange rate. The real effective exchange rate is a weighted average of the bilateral real exchange rates taking into consideration the trade share of its partners in the country’s total trade. There is also the need to look at the issue of currency convertibility. Convertibility of a currency simply means the currency can easily be converted to other currencies without government imposing any restrictions

Devaluation

Devaluation refers to the reduction in the value of a nation’s currency in terms of a specified unit of gold, devaluation is simply the reduction of the value of a nation’s currency in terms of a selected nation’s foreign currency. Subsequently after the IMF ceased the measurement of currencies in gold terms in 1984, these terms are interchangeably used.

Overvaluation

Overvaluation refers to a condition that occurs when the supply of a currency is disrupted by over demand resulting in an increase in its value beyond the accepted market exchange rate. Typically the result of a central bank’s intervention into its money supply in which it buys excess supply of its own currency. An overvalued exchange rate implies that a nation’s currency is too high for the state of the economy. An overvalued exchange rate means that the nation’s exports will be relatively expensive while imports would be cheaper. Overvaluation of a nation’s currency tends to depress domestic demand and encourage imports. An overvalued exchange rate is particularly a problem during a period of sluggish growth. In a booming economy, an overvalued exchange rate can help to reduce inflationary pressure but in a recession, an overvalued exchange rate can cause further deflationary pressures.

2.1.2Exchange Rate Determination Models

Theoretically, there are four (4) general models of exchange rate determination which include; the Traditional Flow Model, the Monetary Model, the Purchasing Power Parity Model and the Portfolio Balance Model. These models would be discussed briefly in this chapter.

The Traditional Flow Model

In this model of exchange rate determination, exchange rate is said to be determined solely by the market forces of demand and supply of foreign exchange. In the Traditional Flow Model, the forces of supply and demand of foreign exchange carry out active responsibilities in determining an equilibrium exchange rate that equates the demand for and the supply of foreign exchange. The Traditional Flow Model was based on the assumption that there are two (2) basic variables whose interactions constitutes the main determinant of exchange rate, and these variables include; variations in interest rates and variations in relative income level. According to Udoye, (2009), this assumption can be justified by the fact that foreign demand for domestic goods is a function of foreign income and vice versa and also asset demand depends on the difference between domestic and foreign interest rates.

However, capital flows plays a vital role in sustaining the equilibrium that the Traditional Flow Model seeks to attain. According to Ogun, (2012), capital flows achieves this with an outflow moderating current account surplus and an inflow financing of the current account deficit. In an attempt to avoid outrageous changes in exchange rates under the Traditional Flow Model, focus is placed more on the factors that bring about variations/changes in the current account such as relative prices and relative income. He further added that a situation of current account deficit which tends to depreciate the exchange rate calls for concerted efforts at reducing both the domestic price level and income while raising the interest rate. Although the implications associated with using capital flows to ease disequilibrium is the unassertive assumption that the asset market obeys only the policy decisions of the country in deficit.

The Portfolio Balance Model

The Portfolio Balance Model of exchange rate determination conceptualizes exchange rate as the result of substitution between money and financial assets in the domestic economy and the substitution between the domestic and foreign financial assets, (CBN, 1998). This model expresses the exchange rate as a function of the resulting process of monetary equilibrium in an economy. According to Ogun, (2012), such financial equilibrium results from a simultaneous equilibrium in the individual financial asset markets, that is when the amount of each asset desired to be held is the exact amount of asset that is actually held.

In this model, there are three (3) vital markets which are assumed to an important role in exchange rate determination and they include; the Domestic Money Market, the Domestic Bonds Market and the Foreign Bonds market and thus three (3) separate equilibrium prices are drawn from the achievement of this financial equilibrium namely; the equilibrium price of each asset, the equilibrium interest rate in the economy and the equilibrium exchange rate. Hence, this model is referred to as the Asset Pricing Approach of exchange rate determination. The exchange rate emerges from this model because any portfolio switches between the domestic and foreign assets necessitates new demand for foreign exchange, (Appleyard et al, ).

The notion here lies in the fact that all economic players have a portfolio choice decision between domestic and foreign assets. Those instruments (either money or bonds) have an expected return that could be arbitraged, this arbitraged opportunity is what determines the process of exchange rate, (Bornbusch, 1988). However, it has been noted that the approach disregards the fundamentals of trade in its calculations, (Ojo, 1998) and this may indeed be a cause for the unexplainable variations in exchange rate, hence its shortfalls.

The Monetary Model

The Monetary Model shares certain similarities with the Traditional Flow Model in that both models depend solely on the Money Market forces of demand and supply to determine the equilibrium exchange rate. This model was developed as a result of the shortcoming of the Portfolio Balance Theory. This model basis importance on money as a unit of exchange and as such views exchange rate as a resultant function of relative shifts in domestic output, money stock and rate of inflation between two (2) trading partners. For instance, according to Ogun, (2012) monetary expansion which could produce an excess supply of money would also cause price levels to rise leading to exchange depreciation. However, the wealth level also increases as a result of this thus leading to an increase purchase of financial asstes. (both domestic and foreign) thus causing an increase in the demand for and supply of money. Inevitably, the excess supply of money would be mopped up and exchange rate returns to its original level in the long run (Frenkel et al, 1978).

Frankel, (1978), stated that this model of exchange rate determination attains equilibrium when existing stocks of money in the two countries are willingly held. The Monetary Model in line with a flexible exchange rate can be viewed from two (2) perspectives; the asset market approach and the monetary approach and it focuses on money as well as other assets as key determinants of exchange rate. In light of the above, it is safe to say that given a situation of a flexible exchange rate, disequilibrium in the money market is short lived and thus exchange rate only changes to support these short run imbalances. Thus, Obioma, (2000), opined that the Monetary Model ascribes changes in exchange rate to income expected rates of return and other factors that influence the demand and supply of money in an economy. As a result, on the basis that supply and demand for money is influenced by the income level, the monetary model speculates three (3) determinants of exchange rate; relative money supplies, relative income and variations in interest rates. The major shortcomings of this model is the assumption that domestic and foreign financial assets can perfectly substitute each other and this may lead to persistent disequilibrium in exchange rate.

Purchasing Power Parity (PPP)Model

The Purchasing Power Parity Model to exchange rate determination has presented and will continue to present a unique view of exchange rate. This model suggests that the exchange rate between two currencies would be equal to the relative national level prices. The PPP Model is based on the assumption that there is a “Law of One Price” in the world. The assumption/law states that all identical goods should be sold at identical prices. According to Investopedia, (2018), the law of One Price is the economic theory that states that the prices of a given security, commodity or asset has the same price when exchange rates are taken into consideration. The law of one price exists due to arbitrage opportunities. The law of One Price implies that exchange rates should adjust to compensate for price differentials across countries (Hoontrakul, 1999). The PPP Model seeks to identify the true equilibrium rate that would ensure simultaneous attainment of current and capital account balances, (Ogun, 2012).

There are two (2) main aspects of the PPP Model; an Absolute version and a Relative version. The Absolute PPP Model states that the absolute level of the exchange rate is that which causes traded commodities to have the same price in all countries when measured in the same currency while in the Relative PPP Model, the equilibrium rate equals a base period exchange rate multiplied by the ratio of the price indices of the domestic and foreign countries, (Ogun, 2012). However, there is very little empirical support for the Absolute PPP Model due to the rather strong influence of transportation costs and trade barriers at keeping prices from equalizing across geographical locations and the effect of the differences in the composition and relative importance of various goods in each country’s price level determination, (Appleyard et al, ). On the other hand, the Relative PPP Model only makes allowance for an implicit rate, the real exchange rate and as such proves less useful in analyzing the trends in explicit rate, nominal exchange rate.

Balance of Payment Approach

Among other existing theories on exchange rate determination is the Balance of Payment Approach. The Balance of Payment Approach of exchange rate determination implies that there is the existence of an internal and external equilibrium in every economy. By internal equilibrium, it assumes that a country has attained full employment and therefore there exists a natural rate of unemployment. By external equilibrium, it assumes that there is a balance of Payment equilibrium. This approach broadens the key deviations of the PPP Model. The major shortcoming of this approach is that it is hard to determine the natural rate of unemployment in general or the exchange rate at which balance of payment attains equilibrium. However, the model can determine where the exchange rate has to converge to, thus providing very little guidiance to the short term exchange rate fluctuations, (Hoontrakul, 1999).

Interest Rate Parity Model

Another popular partial equilibrium exchange rate theory is the Interest Rate Parity Model. This theory attempts to examine how exchange rates are determined in financial markets. Considering that interest rates vary frequently in the short run, the Interest Rate Parity Model is viewed as the short run exchange rate theory. Interest Rate Parity Model is also of two (2) approaches; the Covered Interest Rate Parity and the Uncovered Interest Rate Parity both of which assume the lack of existence of arbitrage and also that asset markets are frictional. This theory however lacks the required empirical evidence to support it as a forward exchange rate determining model.

The Mundell – Fleming Model

The Mundell-Fleming Model is an extension of a closed IS-LM model. However, as opposed to the IS-LM model in which prices are assumed to be flexible, the Mundell-Fleming model assumes that prices are fixed in the short run. Also as opposed to the IS-LM model in which the internal equilibrium in the money market, commodity market as well as the external equilibrium conditions are considered, the Mundell-Fleming Model incorporates the balance of payments. In light of this, the Mundell-Fleming Model is often referred to as a general equilibrium model of exchange rate determination. The Key assumption of this model is that perfect capital mobility, monetary policy, independence and a fixed exchange rate regime can never be attained simultaneously due to the fact that in the long term the level of exchange rate correlates perfectly to the level of money supply and payment monetary policy might only lead to a decrease in economic growth. Also a continuous devaluation may lead to difficulties in the management of Balance of Payments thus causing serious economic problems.

The Dornbusch Model

The Dornbusch Model assumes that prices could be reviewed and used in the determination of exchange rates. In line with the above the Dornbusch Model is generally recommended for gradual price adjustment programs. This indicating that there could exist an overshooting in prices when determining long run equilibrium when there is money shock. At such a point, fixed rates could be an advantage thus implying that in the presence of an economic dislocation business tend to move towards equilibrium and this can be achieved through flexible market or price adjustments. The difference between the two is that in the case of price, adjustments may consume more time and may be less risky than in the case of market. If prices are relatively flexible and inflation is controllable in a medium range, then a fixed exchange rate regime can be favourable.

2.1.3Nigerian Naira Exchange Rate Administration Review

Prior to the implementation of the structural adjustment programme (SAP) in 1956, the Nigerian Naira exchange rate was fixed, that is the rate was settled opposite the US dollar and UK Pound sterling. Despite the fact that this was in accordance with the worldwide practice on exchange rate determination at that point, the framework was observed to be loaded with large twists that prompted wastefulness and misallocation of resources. It also observed that the Nigerian exchange rate did not turn into a policy instrument till 1988 unlike other developing countries. The naira exchange rate was pegged introductory to the British Pound Sterling and in this manner to the United States dollar as a major aspect of international exchange rate administration under the Bretton Woods Framework. Thus the naira exchange rate could be changed just because of a drawn out disequilibrium.

The Nigerian Pound Parity was spelt out in June, 1962 in terms of gold at one Nigerian Pound to 2.48828 grams of gold. This remained same till August 14, 1971, where the Exchange rate of the Nigerian pound of the utilization dollar was dictated by its gold parity. The Naira supplanted the pound as Nigerian money in 1973, and its parity was set at half a pound. Consequently the Exchange rate against the dollar moved toward becoming US $ 1.52 to the Naira. In a month of this the US dollar was the devalued by 10 percent and Nigeria suit with a 10 percent coordinating devaluation, thus keeping up the current Naira-dollar exchange rate. Amid the greater part of 1973, the grapple monetary forms, the dollar and sterling debilitated significantly, managed shortcoming brought into sharp concentration the predicament, characteristic in the strategy for deciding the exchange rate of the Nigerian money.

Be that as it may, in September, 1986, the Second Tier Foreign Exchange Market (SFEM) started as a double Exchange rate framework which created the official first tier rate and the SFEM rate. The previous was authoritatively decided and steadily depreciated. The free market rate which was applicable to different exchanges was dictated by the market powers of demand and supply inside the structure of a foreign exchange auction framework. The quintessence of the double exchange rate framework was to prevent a uniform and sizeable devaluation of the Naira while enabling it to deteriorate in the SFEM while in the meantime, the financial experts proceeded with a descending change of the first tier rate until the point where the two rates unite to deliver a sensible Exchange rate.

This joining which Ojamenaye (1991) has portrayed was accomplished on July 2, 1987 at an exchange rate of N3.74: $1.00. And in line with this improvement, the first teir market was destroyed and unified exchange market (FEM) with a solitary rate that appeared. The FEM additionally grasped the autonomous market, which was permitted to develop. The self-governing section of the FEM was required to be in competition with the parallel foreign exchange market and therefore be appealing to exporters to repatriate their returns.

The presentation of the autonomous market prompted the presence of three trade rates – FEM, rate independent and the parallel market rate which neglected to demonstrate any inclination toward merging. What’s more, as Akinmoladun (1990) has contended the merger of the first teir rate and the SFEM rate was more specialized than genuine as not long after the gap between the auction rate and those of the independent market rates started to develop at a certain point, there was an excess of over 50 percent differential between the two rates and this turned into a wellspring of worry for the monetary authorities. The value differential had the impact of making the auction funds kind of subsidized. The operations of the autonomous market later became destabilizing arising from the tendency towards high arbitrage premium and accusations of authorized dealers of diverting official funds making substantial gains effortlessly (Ojo, 1991).

Different acts of malpractices additionally sprung up as the market authorities or approved merchants were blamed for debasement and apportioning foreign exchange to favored clients. In view of this, the autonomous market was converged with the official segment in January 1989 and the Inter-Bank Foreign Exchange Market (IFEM) was presented. The IFEM involved a day by day bidding framework under which the Apex Bank infused official assets into the market by method for guiding direct sales to the banks as and when reserves were accessible. Different however particular strategies were utilized (single or joined) to decide rates. The daily bidding framework was portrayed by misleading requests for foreign exchange and it reached an end on the 13th December, 1990. The next day, the Central Bank of Nigeria reintroduced the Dutch Auction System (DAS) on a week after week premise.

The framework proceeded all through 1991. Recalling that the Dutch auction system was first presented in 1988, yet was later supplanted by IFEM. The DAS was initially implemented to improve professionalism in the FEM and avoid incredible rate which constantly prompted a steady devaluation of the naira. The system did not achieve this goal. For example, the naira showed heavier deprivation in 1991 compared to the relative stability of the exchange rate in 1990 (Obadan, 1992). The Exchange rate management framework seemed to have been predicated on different techniques that are yet to accomplish the coveted objective. Not just has there been a transformation of the institutional structure from SFEM to FEM to IFEM, there have been steady changes in the operational rules. Other than the Dutch Auction System, the market has tested the different strategies and operational techniques among which are the average and marginal Exchange rate determination or fixed techniques, the week after week, fortnight and every day bidding framework.

In January 1999, Nigeria’s dual exchange rate administration was deserted as the official N22 to a dollar Exchange rate was rejected. Before then, the official rate existed together with the rates on the Autonomous Foreign Exchange Market (AFEM) and was utilized for specific government transactions including external debt services. From that point, the common rate on the AFEM was applied to all foreign trade exchanges. The end of the dual exchange rate framework presented consistency of price in foreign exchange transactions and brought to an end the arbitrage openings made by having an exaggerated official rate next to a market decided rate. It likewise brought more straightforwardness into the government’s money related transactions as just the President had the privilege at that time to figure out which transactions were to be carried out at the official rate. In October 1999, a day by day Inter-Bank Foreign Exchange Market (IFEM) supplanted the AFEM. Under the IFEM regime, the Central Bank of Nigeria’s (CBN) restraining infrastructure on the supply of foreign exchange was expelled as oil companies were permitted to supply foreign exchange to banks directly as opposed to doing so through the Central Bank of Nigeria CBN. The Central Bank of Nigeria’s CBN however remains the chief supplier of foreign exchange in the market and applies impressive impact on the determination of the Exchange rate. In July, 2002, Nigeria reintroduced a bi-weekly Dutch auction system (DAS) as an operational framework for its foreign exchange market to supplant the interbank foreign exchange market (IFEM).

The DAS is a technique for Exchange rate determination via auctions in which bidders pay as indicated by their bid rates and the decision rate is set in line with the last bid rate that clears the market. To put it plainly, as opposed to the old IFEM framework, where supply of currency was flexible at some given rate, leave or take some room for depreciation when request was seen to be too much, under the DAS the Exchange rate is for the most part dictated by bids made by commercial banks in the interest of their customers. So the move back to a DAS demonstrates that Nigeria is by all accounts longing for more, instead of less, flexibility in the Exchange rate and leads one to surmise that Nigeria gives off an impression of wanting to adopt the last monetary administration arrangement: stable prices and a free floating Exchange rate.

2.1.4Exchange Rate Regimes

There are varying options open to nations in terms of adopting and implementing a desired exchange rate regime and these ranges from the fixed regimes to the floating regimes to the other regimes in between them, including the target zones, pegs and the fixed but adjustable regimes. Considering that the adoption of any exchange rate regime is aimed at attaining exchange rate stability, more relevance is placed on the fixed exchange rate and its variants. A fixed exchange rate regime is a system in which the exchange rates of a country are maintained at certain fixed levels. Nigeria implemented and maintained a fixed exchange rate regime from the achievement of its independence in the 1960’s up until the collapse of the Bretton Woods system in the early 1970s. There are two (2) key reasons why fixed exchange rates are considered as more favourable; firstly because they aid the promotion of foreign exchange markets and secondly they promote the possibilities of certainty in international transactions. A few of some of the variants of the fixed exchange rates that would be discussed include;

Crawling Peg Exchange Rate

The crawling Peg exchange rate regime is a middle course between the fixed and the flexible exchange rate regimes. This variant of the fixed exchange rate is more suitable for countries with significant inflation rates as when compared with that of their trading partners. Under this regime, the government fixes the exchange rate on a particular day but adjusts the rates in a pre-announced fashion over time, while taking into consideration the inflation differentials between itself and its trading partners. In general, the Peg can be passive, i.e. the exchange rate is altered with reference to past inflation, i.e. the country announces in advance the exchange rate adjustments it tends to make. The main advantage of the Crawling Peg is that it makes room for the combination of the flexibility required to accommodate different trends in inflation rates between countries while maintaining relative certainty about future exchange rates relevant to exporters and importers. Its disadvantage is that it leaves the currency open to speculative attack because the government is committed on any day or over a period of time to a particular value of exchange rate.

Adjustable Peg Exchange Rate

The Adjustable Peg exchange rate refers to the exchange rate regime in which a country’s currency is pegged to a key currency for instance the US dollar, however, the Peg level could be adjusted occasionally or periodically but within a limited range. According to Udoye, (2009), this regime offers a strong exchange rate commitment and its adherents before the currency crisis of the mid and late 1990s included Brazil, Mexico and Thailand. In these emerging market economies, where capital mobility increased steadily during the 1970s and 1980s and up to a high point in the 1990s the authorities had difficulties in maintaining the peg, (Corden, 2001). However, the Adjustable Peg regime is still favourable for nations with low capital mobility which can be attributed to either the fact that they are not integrated with the capital markets or the fact that they have effective capital controls.

Currency Peg Exchange Rate

In the currency Peg exchange rate regime, a local currency is pegged to an external currency which could either be the currency of a dominant trading partner or a basket of currencies with weights reflecting the shares of the countries in foreign trade. Pegging to a single currency may bring about certain advantages amongst which is the reduction in the exchange rate fluctuations between the focus country and the country to which it is pegged. This in turn favours/encourages trade and capital flows between the two countries. One main shortcoming of the currency peg is that when the currency of a nation is pegged to a floating currency, e.g. the US dollar, the domestic currency will float along with the USD and other currencies as well. Another disadvantage is that movements in the exchange rate in relation to the currencies of other countries may interfere with domestic macroeconomic objectives. In the need to ensure a stable exchange rate, any developing country might decide to peg its currency to a basket of other foreign currencies. Usually, this involves making use of the weighted average of different currency values and pegging the exchange rate at the total trade-weighted (either export or import). A key benefit of this currency peg exchange rate is that it can enable an economy prevent extreme exchange rate instabilities via operating under the currencies of several other trading partners. Also using this currency peg exchange rate a country is better equipped to ensure its nominal effective exchange rate remains stabilized, also it helps in reducing the extent of instable prices which is usually triggered by fluctuating exchange rates. The currency peg however has a disadvantage which is the fact that determining the optimal exchange rate using this method without adequately referring to the local policies of the pegging policy makers. Another disadvantage is that a basket-weighted exchange rate which by definition moves against all major currencies, might reduce confidence on the part of foreign investors and reduce capital inflows (Udoye, 2009).

Target Zone Exchange Rate

This type of exchange rate lies in between the floating exchange rate and the fixed yet adjustable exchange rates and it is well known for its popularity. In this regime, a central exchange rate that can be fixed, made to crawl or to be flexible is set within certain band limits in which the central exchange rate is permitted to roam thus enabling a country to be flexible with its macroeconomic policy objectives. The key advantage of this regime is that it hinders volatility in exchange rate movements.

2.2Review of Theoretical Literature

Exchange rate refers to the rate at which one currency exchanges for another (Jhingan, 2005). The exchange rate of a country can also be defined as the price of its currency relative to the currencies of other countries. According to Mankiw (1997), it can also be defined as the price at which exchanges between two countries takes place. The process of determining the optimal exchange rate in a country is an undeniable matter at the forefront of finance and global economics in the world today. In Nigeria, the monetary authorities are constantly seeking to attain an optimal, non-volatile and favorable exchange rate in their bid to ensure the attainment of all macroeconomic goals, which is due to the fact that exchange rate instabilities and volatilities would inevitably exert a strenuous influence on private and foreign investments, prices of goods and services as well as export and import decisions. Ojo (1998) opined that a realistic exchange rate is one that reflects the strength of foreign exchange inflow and outflow, the stock of resources as well as ensuring an equilibrium in the balance of payments that is consistent with the cost and price levels of all trading partners.

The effects of exchange rate on output changes has long been established in several economic literatures on this subject matter however, there has been a lack of consensus to show the direction of these effects. According to the traditionalists, a fall in exchange rate is likely to enhance trade balance, prevent balance of payments deficits and subsequently increase output and employment as long as the Marshall-Lerner conditions are fulfilled. The Marshall-Lerner condition states that depreciation would lead to expansion in output if the sum of price elasticity of demand for export and the price elasticity of demand for imports is greater than unity, (Investopedia, 2018). According to Taye (1999), the mechanism behind these positive effects, is that devaluation switches demand from imports to domestically produced goods by increasing the relative prices of imports and making export industries more competitive in international markets thus stimulating domestic production of tradable goods and inducing domestic industries to use more domestic inputs. On the other hand, the monetarists believe that exchange rate changes do not impact macroeconomic variables on the long run. They believe that exchange rate devaluation affect real magnitudes mainly through real balance effect in the short run but leaves all real variables unchanged in the long run (Domac, 1977). This belief is only valid on the assumption that the purchasing power parity (PPP) holds. It inevitably shows that a rise in the exchange rate causes an increase in output in the short run thereby enhancing the balance of payment. However, devaluation although increases output and improves BOP also leads to a rise in prices in the long run.

Having established the likely influences exchange rate has on investment (private and foreign) balance of payment equilibrium, interest rate and inflation, the act of establishing a decent exchange rate is very vital in the successful attainment of all developmental plans and programs in an economy. According to Chuka, 1990, the objectives of exchange rate policies are to increase output and ensure its optimal distribution. One constant requirement for the successful achievement of a nations objectives is an exchange rate that is stable. He also opined that stability permits viability of the rate in response to changes in relative prices, international terms of trade and growth factors.

The whole concept of exchange rate policy had to do with determining an exchange rate regime and choosing a specific rate to guide all foreign transactions. Exchange rate policy influences the growth of an economy by ascertaining capital flow, direct and foreign investment as well as its external balance. A lack thereof in foreign exchange leads to difficulties in the development of a nation. In attaining development, the growth rate of a country’s output as well as its demand for imports tend to outweigh its export based volume hence causing a clash between advancing its internal development while conserving its external balance. This clash is usually solved by appointing a favorable exchange rate policy.

Exchange rate policies that involve either a devaluation or an over valuation of a country’s currency always comes attached with certain consequences. Overvaluation of foreign exchange enhances external borrowing, balance of payment deficit and economical disintegration while according to Sodersten, (1997), undervaluation leads to income distortions and bias which is detrimental to labour, trade, current account surpluses, standard of living and the growth of the economy. On the other hand, devaluation enhances external competition via a reduction in imports or an enhancement of exports which on the long run favours both consumption and investment decisions.

The key requirement of an efficient exchange rate regjme is the assumption that exchange rate reflects the relative productivity of an economy, (Obadan, 2003). On the long run, the devaluated currency in this case the Naira helps to barricade all local industries while facilitating an increased local production, a decreased cost of all imported raw materials thus enhancing the competitiveness of locally made goods. There are various elements that guide the selection of a specific exchange rate regime of a nation, one of which is a nation’s intricate economic conditions or foundations, its external economic fundamentals as well as the responsive effects of all random shocks on the local economy. In other words, an economy such as the Nigerian economy where there is high vulnerability to an unstable internal financial environment as well as external shocks (i.e. Shocks from terms of trade or excess debts) always seeks to implement an exchange rate system that can be easily maneuvered. It is generally believed that the fixed exchange rate regime is favorable if an economy is prone to internal shocks while a flexible exchange rate system is favorable if an economy is prone to external shocks, it is nonetheless becoming increasingly recognized that whatever exchange rate regime a country may adopt, the long term success depends on its commitment to the maintenance of strong economic fundamentals and a sound banking system, (Sanusi, 2004).

According to Meese and Rogoff, (1990), the floating exchange rate was adopted in developing countries from 1973 and the question of whether exchange rate uncertainties have independent adverse effect on the transactions of a country has attracted a lot of literature. They also opined that the introduction of adjustment programs by many of these countries and the attendant liberalization of exchange rates have brought the discussion of this work farther into global focus. Udoye, (2009) stated that economists are divided over whether government’s arguments for managing exchange rates rest on one of the following key notions; first, that the government can determine the fundamental equilibrium exchange rate; secondly, floating exchange rate has been too volatile; and third, under-floating exchange rate currencies can become significantly over valued or undervalued. Williamson, (1994), opined that supporters of the floating exchange rate point out that exchange rate volatility may or may not have adverse effect on favorable terms of trade depending on its effect on import. Therefore exchange rate volatility or fluctuation can be positive or negative.

According to the work of Taylor, (1995), in which he categorically opined what is now popularly referred to as the Purchasing Power Parity theory that the value of a foreign currency in terms of another depends mainly on the relative purchasing power of the two currencies in their respective countries, i.e. the exchange rates settles at the level which makes the purchasing power of a given unit of currency the same in another country in which it is spent. He debated that the theory fails in certain areas, for example; a change in the exchange rate may originate independent of the price level thus stipulating that the Purchasing Power Parity does not, as believed give a total understanding of what determines the exchange rate but notwithstanding the fact that the theory holds certain truths.

According to Krueger, (1983), stated that in a completely free exchange market, exchange rate would fluctuate freely in response to varying demand for the different currencies. With fluctuating demand for currencies, greater savings in foreign exchange rate can be achieved due to the fact that capital movement directly impacts exchange rate like in the case of exports and imports. Conversely, as far as demand for and supply of other different currencies remains in balance, stable exchange rate would prevail under free exchange markets. Krueger, (1983), also added that how the exchange rate is determined depends on whether the rate is a fixed exchange rate or a floating exchange rate. Recall that a fixed exchange rate is one in which the exchange rate is set according to the stipulations or decree of the government usually within a limited variance range while a floating exchange rate is one that is solely (freely) left to be determined by the market forces of demand and supply.

2.2.1Review of Existing Literature on Exchange Rate and GDP

Several studies have been carried out in attempt to understand the relationship between Exchange rates and economic growth (GDP). Aliyu (2011) opined that appreciation of exchange rate results in increased imports and reduced export while depreciation would expand export and discourage import. It is important to note that depreciation (a fall) of exchange rate leads to a movement from foreign goods to domestic goods thus leading to movement of income from the importing nations to the nations exporting via a shift in terms of trade, which subsequently impacts positively on the exporting nations’ economic growth and negatively on the importing nations’ economic growth.

With the same concept, Hossain (2002) supported that exchange rate helps to connect the price systems of two different countries by making it possible for international trade and also effects on the volume of imports and exports, as well as country’s balance of payments position. Rogoffs and Reinhartl (2004) also suggested that developing countries are relatively better off in the choice of flexible exchange rate regimes. Also, Edwards and Levy Yeyati (2003) discovered evidence to prove that nations with a more flexible exchange rate grow faster. Faster economic growth is significantly associated with real exchange rate depreciation (Hausmann, Pritchett, and Rodrik 2005).

Rodrik (2009) also opined that real undervaluation promotes economic growth, increases the profitability of the tradable sector, and leads to an expansion of the share of tradable in domestic value added. He argues that the tradable sector in developing countries can be too small because it suffers more than the non-tradable sector from institutional weaknesses and market failures. According to him, a real exchange rate undervaluation works as a second-best policy to compensate for the negative effects of these distortions by enhancing the sector’s profitability. A Higher profitability encourages investment in the tradable sector thus leading to its expansion and subsequently encourages economic growth. Other past researches on the effects of exchange rate on economic growth has failed to reach an agreeing summary. There are empirical evidences to show that real exchange rate instabilities has a large effect on growth outcomes. Asher (2012) investigated the impact of exchange rate fluctuation on the Nigeria economic growth for period of 1980 – 2010. His findings revealed that real exchange rate has a positive effect on the economic growth. In the same vein, Akpan (2008) examined foreign exchange market and economic growth in an emerging petroleum based economy from 1970-2003 in Nigeria. He discovered that positive relationship exists between exchange rate and economic growth. In their work, Obansa et al (2013) also studied the relationship between exchange rate and economic growth in Nigeria between 1970 – 2010. Their findings revealed that exchange rate has a strong impact on economic growth. They summarized that exchange rate liberalization was good to Nigerian economy as it promote economic growth. Azeez et al (2012) also examined the effect of exchange rate volatility on macroeconomic performance in Nigeria from 1986 – 2010. Their findings revealed that exchange rate is positive related to Gross Domestic Product.

Adebiyi and Dauda (2009) applying the error correction model stated in contrast that trade liberalization promoted growth in the Nigerian industrial sector and stabilized the exchange rate market between 1970 and 2006. According to them, there was a positive and significant relationship between index of industrial production and real export. A one per cent rise in real export increases the index of industrial production by 12.2 per cent. Thus implying that the policy of deregulation impacted positively on export through exchange rate depreciation.

Ghosh et al (1997) carried out a descriptive analysis of the growth performance under alternative regimes in 145 IMF-member countries for 30 years after 1960 and discovered a slightly higher GDP growth under a float (1.7% under floating compared to 1.4% under a peg). They concluded that as investment rates contributed two percentage points of GDP, then the lower output growth under a peg must be a result of a slow productivity growth and that higher productivity growth under a float also supported the growth of external trade. However, the growth showed to be the higher (2%) under an intermediate regime (soft pegs of managed float) and highest (an extra 1%) under a floating regime.

2.2.2Review of Existing Literature on Exchange Rate and Inflation

Inflation is defined as the rate at which general prices of goods and services persistently rise over a specific period of time with adverse consequent effect of an increase in the demand for goods and services which is relative to limited supply of these goods and services, (Investopedia, 2018). Therefore, inflation is the persistent rise in the general price level over a period of time. Inflation has fatal consequences on the function of money as a medium of exchange and store of value. Hence a persistent increase in general price level equals that each unit of the currency can only purchase a less than required amount of goods and services.

According to Nnamocha (2002), inflation is a sustained rise in the general level of prices. Inflation impacts on an economy by raising the opportunity cost of demand for money, in other words, the uncertainty people have over future rise in prices is likely to discourage savings and investment. High inflation is in most cases accompanied by an increased money supply however, money supply does not categorically lead to inflation just as long as it increases in the same proportion as output. The monetary and traditional flow theories both provide a theoretical basis for this subject matter. The monetary approach to exchange rate determination suggests that the relative supply of and demand for money between two trading partners is the basis for the determination of exchange rate. It opines that an increase in the supply of money is able to generate inflation, hence, resulting in exchange rate depreciation.

The model suggests that a case of falling prices with a given money supply leads to exchange rate depreciation, while the traditional flow model is essentially based on the principle of the interplay of demand and supply. The forces of the market (interaction between demand and supply) determine the rate of exchange. However, when there is speculation or expectation of a change in the rate of exchange, this could lead to the disequilibrium even without any change in the initial determined factors. Ettah, et. al (2012) in their study of the effects of price and exchange rate fluctuations Agricultural exports in Nigeria observed that exchange rate fluctuations and Agricultural credits positively affect cocoa exports in Nigeria. They also showed that the relative prices of cocoa are insignificantly related to quantity of export, but however carried a negative sign which is in line with a priori expectation. The implication of this is the volatility on cocoa export in Nigeria.

Asher (2012) stated that exchange rate is used to determine the level of output growth of the country. Although, with already existing exchange rate policies, a constant exchange rate has been the key uncertainty in the trade transaction. Thus leading to declines in standard of living of the population as well as increase in costs of production thus resulting in cost-push inflation. Ndung’u (1993) carried out an estimation of a six-variable VAR—money supply, domestic price level, exchange rate index, foreign price index, real output, and the rate of interest—in an attempt to explain the inflation movement in Kenya. He observed that the rate of inflation and exchange rate explained each other. A similar conclusion was also reached in the extended version of this study (Ndung’u, 1997).

2.2.3Review of Existing Literature on Exchange Rate and Trade Openness

One of the main aim for determining an optimal exchange rate is to promote the greater openness of economies. Thus, it highly is likely that there is a causal relationship between these two variables. One of the studies focusing on these factors was carried out by Hau (2002). In his study, he carried out an analysis of the openness of an economy and its impact on real exchange rate movements. He argued that trade integration and real exchange rate volatility are structurally linked and that there is a negative correlation between them. In his research, he employed a small open economy model with a tradable and a non-tradable sector. According to his findings, he concluded that economies which are more open have a more flexible aggregate price level. This flexibility in turn reduces the effect of unanticipated money supply shocks thus resulting in lower real exchange rate volatility for countries with greater openness of the economy. Hau further supported his argument with an empirical study using a sample of 48 countries over a 19-year time period. As a proxy for openness he employed an import vs. GDP ratio. With Real exchange rate volatility measured as the standard deviation for the percentage changes of the effective real exchange rate over intervals of 36 months, his findings confirmed the impact of an economy’s openness on exchange rate determination as openness explained almost half of exchange rate variations. However, Hau’s results did not categorically state the particular countries used as each country was represented by arithmetic mean values.

Stancik (2007) examined the sources of exchange rate volatility among European Union members’ countries. The study employed the threshold autoregressive conditional heteroscadasticity (TARCH) model it’s method of analysis. His results showed that economic openness, information and flexible exchange rate regimes have positive and statistically significant impact on exchange rate volatility and determination.

Also, Al-Samara (2009) examined the determinants of real exchange rate volatility in Syria over the period of 1980 to 2008. The specific objective of his research was to identify the principal factors suggested in many theoretical literatures, which includes relative productivity, government expenditure, terms of trade, trade openness and net foreign assets. His results revealed that there was a high causality relationship existing between trade openness of the economy and its exchange rate instabilities.

2.3Review of Empirical Literature

Jimoh, (2006) analyzes the Nigerian information from 1960 to 2000 to understand what backing it offers to the traditional theory of real exchange rate. He utilized the notable Johanson’s (1992) techniques for assessing models whose variables were non-stationary yet co-integrated, the investigation discovered that the definitive trade liberalization program of 1986 – 1987 prompted upto about 13% depreciation in the Nigerian real exchange rate and made the real rate more receptive to changes in its terms of trade. He likewise discovered that less definitive changes in trade administration created no tangible changes in the real exchange rate.

Shehu and Aliyu, (2006), in their study sought to assess the long run behavioral equilibrium exchange rate in Nigeria. They utilized quarterly information from 1984Q1 to 2004Q4 and came up with a Behavioral Equilibrium Exchange Rate (BEER) and a Permanent Equilibrium Exchange Rate (PEER). Regression results demonstrated that a large portion of the long-run behavior of the real exchange rate could be explained by real net foreign assets, terms of trade, index of crude oil volatility, record of monetary policy outcomes and government fiscal position. Based on these basics, four scenes each of overvaluation and undervaluation were recognized and the forerunners portraying the scenes were similarly followed to the archive of exchange rate in the nation within the survey period. Among others for example, expansive inflow of oil incomes into the nation and stable macroeconomic performance were found to account for undervaluation of the real exchange rate in the vicinity of 2001Q1 and 2004Q4 in Nigeria. The results additionally recommended that deviations from the equilibrium point are eliminated within one to two years.

According to Agnès and Coeuré (2001), in their paper indicate how the traditional trade off amongst adjustment and disinflation can deliver soft pegs as ideal exchange rate administrations notwithstanding if the monetary fragility and the cost of administration switches in terms of credibility are considered. The ideal level of exchange rate flexibility relies upon the structural qualities of the nation and on the inclinations of monetary authorities. Their findings is affirmed by cross-section logit estimation for 92 nations before and after the 1997-1998 developing markets crisis relating exchange rate administration decision with the nations structural patterns. The model effectively predicts up to 86% of the studied administrations and a portion of the current ones move towards the hard pegs.

Devereux and Engel (1988) specifically analyze how price setting influences the ideal decision of exchange rate system. They discover that when prices are set in consumers’ currency, floating exchange rates dependably rule fixed exchange rates. At the point when prices are set in producer’s currency, there is a trade-off amongst the floating and the fixed exchange rates. Exchange rate modifications under floating rates takes into account a lower variance of consumption, however exchange rate unpredictability itself prompts a lower average level of consumption. The conclusion from the basic analysis of their study shows that, if the exchange rate is unstable, fixing exchange rates to both US dollar and Japanese Yen is superior to floating, in light of the fact that both US and Japanese exporters set their prices in producer’s currency.

Also, Devereux and Engel (2000) examined the decision of exchange rate systems – fixed or floating in a changing, inter-temporal general equilibrium structure. They utilized a broadened Devereux and Engel (1998) framework to researching the ramifications of internationalized production. They looked at the part of price setting – whether prices are set in the producer’s currency or the consumer’s currency – in deciding the optimality of exchange rate systems in an environment of instabilities caused by financial shocks. They found that when prices are set in producer’s currencies, floating exchange rates are suitable when the nation is sufficiently expansive, or not highly prone to risks. Then again, floating exchange rates are always suitable when prices are set in consumer’s currencies because floating exchange rates enable local consumptions to be protected from foreign financial shocks. The benefits from floating exchange rates are more prominent when there is internationalized production in this case.

Engel (2000) examined the ideal exchange-rate policy arrangement in two-nations, he utilized sticky-price general equilibrium models in which household units and firms improve over an interminable horizon in an environment of instabilities. The models are in the vein of the “new open-economy macroeconomics” as exemplified by Obstfeld and Rogoff (1995). The conditions under which fixed or floating exchange rates yield higher welfare, or the ideal foreign exchange mediation rule, rely upon the precise nature of price stickiness and on the level of risk sharing avenues. The investigation gives some preliminary empirical evidence on the behavior of consumer prices in Mexico that indicates short comings on the side of the law of one price are crucial. The proof on price setting and risk sharing opportunities isn’t sufficiently refined to influence authoritative decisions about the ideal exchange rate administration fit for that nation.

Amartya et al (2004) revisited the issue of the optimal exchange rate system in a flexible price environment. The key development is that he broke down the inquiry with regards to situations where just a small amount of agents partake in asset market transactions (i.e., resource markets are divided). He demonstrated that flexible exchange rates are ideal under financial shocks and fixed exchange rates are ideal under real shocks. These discoveries are the correct inverse of the standard Mundellian findings inferred under the sticky price worldview wherein fixed exchange rates are ideal if financial shocks rule while flexible exchange rates are ideal if shocks are for the most part real. This outcome subsequently recommends that the ideal exchange rate administration ought to depend not just on the kind of shock (financial versus real) but additionally on the type of rubbing (goods market versus financial markets).

In his study, Chuka, (1990) revealed that there is no such thing as ”the” ideal or best exchange rate policy. Everything relies upon the basic fundamentals, which might be either local or foreign. How nations respond to them won’t be the same by any means. Floating the currency would, obviously, be esteemed to be preferable to alternate methodologies yet answers should still be provided as to, among others, whether the nation has an adequate resource reserve to mediate in the market consistently when it is required. On account of Malawi, this has turned out to be exceptionally troublesome since the accessibility of foreign exchange is exceedingly occasional. He summarized that Malawi additionally faces another issue in that public trust in the floating administration is taking rather long to stabilize with the consequence that the kwacha is constantly under speculative attacks.

El-Mefleh (2004) examined the correct exchange rate regime that serves primary macroeconomic objectives while expanding incorporated worldwide financial markets. The significant discoveries of the examination were; (1) It is implausible to expect that one exchange rate system is the best for all conditions and for all nations; (2) The decision of pegging the currency of a nation to the currency of another nation or to a basket of currencies relies upon the level of trade concentration with another nation (nation B) and the currency in which the nation’s (nation A) foreign debt is mostly denominated; and (3) The manage or free float regime is more optimal for a nation exceptionally incorporated into worldwide financial markets.

Barnett and Kwag (2005) incorporated the aggregation and index number theory into fiscal models of exchange rate determination in a way that is internally coherent with money market equilibrium. Divisia financial totals and user-cost are concepts utilized for money supply and opportunity-cost factors in the monetary models. They estimated a flexible price monetary model, a sticky price monetary model, and the Hooper and Morton (1982) model for the US dollar/UK pound exchange rate. They compared forecast results using mean square error, direction of change, and Diebold-Mariano statistics. They discovered that models with Divisia indexes are preferable to the random walk assumption in explaining the exchange rate fluctuations.

Leo (2006), investigated the welfare ramifications of a small nation’s exchange rate system, for the small nation itself, and additionally for a substantially larger nation, the currency of which the small nation potentially pegs its currency to. A two-nation dynamic stochastic general equilibrium model was created for the analysis. Floating Exchange rate systems were modeled as Taylor type interest rate rules, with various feedback coefficients on inflation and output. He demonstrated that contrasted with a fixed exchange rate system, the two nations would be worse off if the small nation adopts an interest rate rule with a large feedback coefficient on inflation. He additionally demonstrated that it is critical for the small nation not to react to output changes in its interest rate rule, as it will produce exorbitant fluctuations of inflation.

Batini and Levine (2006), in their investigation of Optimal Exchange Rate Stabilization in a Dollarized Economy with Inflation Targets demonstrated that First, dollarization complicates the conduct of monetary policy; however monetary policy can in any case be successfully carried out and with low expenses in terms of real activity under dollarization if the national bank commits to an inflation target. In this way, introducing an inflation target in somewhat dollarized economies can decrease the cost of price stabilization. Secondly, regardless of whether the level of dollarization depends endogenously on the reaction of monetary policy from the exchange rate, it is as yet attractive to ‘smooth’ the exchange rate, in addition to correcting deviations of expected inflation from target. In this sense, an ideal straightforward rule for a partially dollarized economy varies from that of a non-dollarized economy, in that in the previous economy there are significant benefits from including an exchange rate term in the rule, in opposition to regular discoveries on similar rules for non-dollarized economies. Abstracting from the numerous other unfavorable outcomes of dollarization, the discoveries demonstrate that nations with no credibility may profit from partial dollarization in that it hinders monetary policies from being conservative. Thirdly, exchange rate smoothing diminishes the odds of multiple equilibrium under dollarization.

Faia, (2005) studied the optimal choice of exchange rate systems in a two nation model with sticky prices and matching frictions in the labour market. Currency fluctuations by influencing the price of tradable goods have a tendency to intensify movements in and out of the labour market and the volatility of vacancy creation which in turn tends to increase overall macroeconomic instabilities. Consequently and in spite of the popularly known protecting properties of currency fluctuations, monetary authority (Faia, 2005) can accomplish domestic stabilization and increase welfare by having exchange rate as an independent target in the monetary policy rule. The examination additionally demonstrated that the model presented is compatible with well-known stylized facts of both the international transmission of shocks (such as positive co-movements of output and employment) and of the labour market (such as the Beveridge curve, the pro-cyclicality of labour market tightness and the high volatility of labour market variables).

Benigno and Benigno, (2004) proposed a theory of exchange rate determination under interest rate rules. They demonstrated that simple interest-rate feedback rules can determine a unique and stable equilibrium without any explicit reaction to the nominal exchange rate in their two-country optimizing model with sticky prices. They portrayed how the behavior of the exchange rate and the terms of trade depend vitally on the monetary policy chosen, though not necessarily on monetary shocks. They gave a simple account of exchange rate volatility in terms of monetary policy rules; they provide an explanation of the relation between nominal exchange rate volatility and macroeconomic variability in terms of the monetary regime adopted by monetary authorities.

Bruno and Pugh (2006) investigated the trade impacts of exchange rate variability on international economies for the past 30 years. The study applied meta-regression analysis (MRA). They discovered that, on average, exchange rate variability exerts a negative impact on global trade. In addition, MRA helped to explain the wide variation of results in this literature ranging from significantly positive to significantly negative effects and suggests new lines of enquiry. In particular, their results suggested a regime effect, whereby the trade effect of exchange rate variability is conditioned by the institutional environment.

Kandil (2004) examined the effects of exchange rate fluctuations on real output growth and price inflation in a sample of twenty-two (22) developing countries. The analysis introduced a theoretical rational expectation model that decomposes movements in the exchange rate into anticipated and unanticipated components. The model demonstrates the effects of demand and supply channels on the output and price responses to changes in the exchange rate. In general, exchange rate depreciation, both anticipated and unanticipated, decreases real output growth and increases price inflation. The evidence confirms concerns about the negative effects of currency depreciation on economic performance in developing countries.

2.4Summary of Literature Reviewed

From the vast pool of literatures reviewed, it was observed that;

First, most studies on exchange rates either focused on the impact of exchange rate volatility on trade or on growth with very few focused on the key variables and factors that determine exchange rates.Second, majority of the studies were carried out outside the shores of Nigeria. Thus confirming the need to carry out this research.

Third, a vast majority of the studies on the determinant of exchange rate did not consider the possibilities of periodical/terminal relationship (long run and short run) between exchange rate and their macroeconomic variables that determinant it, although, Shehu and Aliyu (2006) estimated the long run behavioral equilibrium exchange rate in Nigeria using quarterly data from 1984Q1 to 2004Q4. It is quite safe to say that this period is too short a time to access the long run behavior of exchange rate and its macroeconomic variables since most time series analysis require a minimum of 25 years observation for time series data.

CHAPTER THREE

METHODOLOGY

This chapter seeks to identify and explain the stages with this research work would follow, from the research design to be implemented when carrying out this study, to the types of data required as well as the sources of these data. This chapter would also seek to investigate the econometric and statistical tools that would be used to analyze the data obtained while justifying the reason for such tools.

3.1Research Design

The research design that would be applied to this study is the Quasi-experimental design. This particular design was chosen based on the fact that this study seeks to ascertain the key determinants of exchange rate and the relationships existing between such variables. Also, the variables intended to be analyzed would be sourced only from secondary (i.e. already existing data) and as such would not require the use of other research designs such as the survey design.

This research would implement a simple linear specification of the multivariate time series function applying the partial adjustment approach to estimate known parameters of a model. In this vein, the Autoregressive Distributed Lag Model (ADLM) would be employed because previous values of exchange rate tends to influence the present value and also there is the tendency that the existing relationship between the exchange rate in Nigeria and its macroeconomic determinants have a dynamic nature.

In order words, these variables that determine the exchange rate in Nigeria may influence other than the present period. However, The ADLM model employed would account for the joint estimation of relationships between exchange rate in Nigeria and its determinants. In addition, there might be a need to transform the model into an Autoregressive Distributed Lag Error Correction (ADL-ECM) Model should there be proof of the presence of co-integration among the determining variables. The ECM would help to reveal both the long run and the short run relationships existing between exchange rate in Nigeria and its determining variables.

3.2Model Specifications

According to Williamson, (1994) a nation’s ideal exchange rate’s determinants are basically its macroeconomic variables (i.e. the key macroeconomic variables) and also that the long-run value of the real exchange rate is determined by the permanent values of these variables, and as such the model specification to guide this study is formulated as follows;

RER = F(GDP, INF, MPR, OILP, TOP,)……………….(1)

Long-run expression of the model

lnRERt=?0+?1lnGDPt+?2INFt+?3lnMPRt+?4OILPt+?5TOPt+?t (1) The general expression of a short-run dynamics model is specify as follows

?lnRERt=?0+i=1m?1?lnRERt-1+i=1n?2?lnGDPt-1+i=1o?3?INFt-1+i=1p?4?MPRt-1+i=1q?5?lnOILPt-1+i=1q?6?lnTOPt-1+?7ECMt-1+?t (2)Where;

RER = Real Exchange Rate

GDP = Gross Domestic Product

INF = Inflation Rate

TOP =Trade Openness

OILP = Crude Oil Prices

MPR = Monetary Policy Rate

Where;

?0 = the constant term

?1, ?2, ?3 and ?4 = the parameters to be estimated

µ =error term

The effects of the explanatory variables on the dependent variable in the model specified above might not be automatic, i.e. it is hardly instantaneous. Gujarati, 2004 opined that sometimes the dependent variable responds to the explanatory variables over a period of time. Therefore, equation (2) when transformed into a dynamic model becomes:

3.2.1Model Justification

There are different models available to economic scholars during model specifications. However the choice of a particular model is often based on its reliability, effectiveness and adequacy. Thus among the numerous rival models, the Autoregressive Distributed Lag Error Correction Model (ADLMECM) is chosen for this research and this is due to the fact that, apart from the efficiency of the ADLM, it would also help to draw inferences about dynamic behavior of the variables since it has been established that it takes into account the lapses of time for the dependent variables to respond to the explanatory variables when modeling. Also, the ECM will be most appropriate and efficient model that can capture the long run behavioral pattern of variables under co-integration situation (See Enders, 1995). Lastly, the model is intended to solve the problem of the so-called “spurious” regression associated with non-stationary data.

3.3Data Required

The data needed for this study would be secondary in nature. Secondary data on the following variables would be required;

Oil price (1980 – 2016)

Monetary Policy Rate (1980-2016)

Inflation (INF) (1980- 2016)

Trade Openness (TOP) (1980-2016)

Exchange Rate (EXR) (1980-2016)

3.4Data Collection and Sources

All the data employed in this study are secondary in nature and as such would be obtained from Secondary Sources. These secondary sources would include the Central Bank of Nigeria(CBN) Statistical Bulletin and Annual Reports, journals and text books are also to be consulted.

3.5Method of Data Analysis

In this research study, the Ordinary Least Square method would be employed to estimate the relevance variables stated in the model. The OLS method has been successfully used in a large number of economic researches involving relationships. This method makes use of a sound statistical technique required for empirical problems; also the method has become standard enough to ensure that all estimates are presented as a reference point even at times when results from any other estimation techniques are used. In addition, the accuracy of this method depends on the fact that it is efficient, consistent and unbiased thus its error term has a minimum and equal variance. The conditional mean value is zero and normally distributed (Gujarat, 2004).

3.5.1Unit Root Test

The global assumption in creating, specifying and analyzing economic models is that all underlying variables are stationary; however this has been proven to be generally untrue. Before proceeding to estimate the model as specified in equation (3) above, there is a need to test for the time series properties of the data obtained. This is required as time series economic scholars in the likes of Granger and Newbold, (1974); Dickey and Fuller, (1981) Eagle and Granger, (1987),; Enders, (1995); Pindyck and Rubinfeld, (1998), etc., have overtime discovered that results obtained from most macroeconomic variables tend to be spurious in nature and to prevent this the time series properties of time series data must first be examined. Therefore, the time series properties of the data used in this research would be tested by employing the Augmented Dickey Fuller (ADF) test as well as the Engle-Granger co-integration procedure. The testing procedure for the ADF is specified as follows:

?RERt=?0 +?t +??RERt?1+?1?RERt?1 +…+?p?RERt?p+Ut………(4)

Where 0= constant,

t = the coefficient on a time trend

p = lag order of the autoregressive process

? = the difference operator.

The unit root test would then be carried out under the null hypothesis (= 0) against the alternative hypothesis (< 0). If the test statistic is greater (in absolute value) than the critical value set at 5% or 1% level of significance, then the null hypothesis (= 0) is rejected and no unit root is present.

From the above, the model specified in equation (3) becomes;

?RERt=?0+ ?1?RERt?i + ?2?GDPt?i + ?3?INFt?i + ?4?DOPYt?i + ?5?TOPt?i +µ………….(5)

Where,? = the difference operator.

3.5.2Co-integration test

The test for possible long-term relationship between the dependent variable and the explanatory variables are carried

?1ut?1 = Error Correction Representation

?1 = Coefficient measuring the degree of error corrected

Hence, the model in equation (6) is the Autoregressive Distributed Lag Error Correction Model (ADLECM) that needs to be estimated if there is evidence co-integration among the variables. On the other hand, if there is no co integration among the variables Autoregressive Distributed Lag Model specified in equation (5) would be estimated.

This research would employ three basic criteria in evaluating the results obtained from the model (s) specified above, these criteria include; economic (a priori expectations), statistical and econometric criteria. The economic criteria would inform if the signs of the variables coefficient conform to standard economic theories. While the Statistical criteria would focus on testing the significance of the variables using the T-test and also the F-statistic will be used to assess the joint significance of the overall regression in order to ascertain whether the model is well specified. Also, the econometric criterion would involve such tests as autocorrelation and multicollinearity. The autocorrelation test would help to check for the existence of serial correlation among the variables, while the multicollinearity test would help to check if the variables are collinear.

CHAPTER FOUR

RESULTS AND DISCUSSIONS

We presented data and discusses results in this section of the project. The section is climax with the discussion of findings.

YEAR RER RGDP

(N’ Billions) INF TOP OILP

($’ US) MPR

1981 0.6100 15,258.00 20.81282 16.47636 34 6.00

1982 0.6729 14,985.08 7.697747 12.24487 32.38 8.00

1983 0.7241 13,849.73 23.21233 10.06517 29.04 8.00

1984 0.7649 13,779.26 17.82053 9.547196 28.2 10.00

1985 0.8938 14,953.91 7.435345 9.769117 27.01 10.00

1986 2.0206 15,237.99 5.717151 7.362417 13.53 10.00

1987 4.0179 15,263.93 11.29032 19.3323 17.73 12.75

1988 4.5367 16,215.37 54.51122 16.43266 14.24 12.75

1989 7.3916 17,294.68 50.46669 21.19088 17.31 18.50

1990 8.0378 19,305.63 7.3644 31.14093 22.26 18.50

1991 9.9095 19,199.06 13.00697 35.40399 18.62 14.50

1992 17.2984 19,620.19 44.58884 38.33388 18.44 17.50

1993 22.0511 19,927.99 57.16525 30.53042 16.33 26.00

1994 21.8861 19,979.12 57.03171 20.92383 15.53 13.50

1995 21.8861 20,353.20 72.8355 58.91781 16.86 13.50

1996 21.8861 21,177.92 29.26829 49.53967 20.29 13.50

1997 21.8861 21,789.10 8.529874 50.76755 18.86 13.50

1998 21.8861 22,332.87 9.996378 34.63236 12.28 14.31

1999 92.6934 22,449.41 6.618373 38.65359 17.44 18.00

2000 102.1052 23,688.28 6.933292 42.49008 27.6 13.50

2001 111.9433 25,267.54 18.87365 39.66164 23.12 14.31

2002 120.9702 28,957.71 12.87658 28.73985 24.36 19.00

2003 129.3565 31,709.45 14.03178 38.8535 28.1 15.75

2004 133.5004 35,020.55 14.99803 38.04465 36.05 15.00

2005 132.1470 37,474.95 17.86349 45.11631 50.59 13.00

2006 128.6516 39,995.50 8.239527 36.40021 61 12.25

2007 125.8331 42,922.41 5.382224 37.04067 69.04 8.75

2008 118.5669 46,012.52 11.57798 5.157526 94.1 9.81

2009 148.8802 49,856.10 11.53767 4.56252 60.86 7.44

2010 150.2980 54,612.26 13.7202 3.705615 77.38 6.13

2011 153.8616 57,511.04 10.84079 3.21027 107.46 9.19

2012 157.4994 59,929.89 12.21701 2.822627 109.45 12.00

2013 157.3112 63,218.72 8.475827 2.523924 105.85 12.00

2014 158.5526 67,152.79 8.057383 2.270862 96.29 14.00

2015 193.2792 69,023.93 9.017684 2.149895 52.65 14.00

2016 253.4923 67,931.24 15.69685 2.001879 43.55 14.00

Source: CBN Bulletin, 2016.

Explain the trend

Table 4.2: Unit Root Results and Order of Integration

Variables ADF PP Decision

Level 1st_diff. Critical Values Level 1st_diff. Critical Values L(d)

InRERt-1.934 -5.022*** -2.9484 -2.0818 -5.022*** -2.9511 I(1)

lnGDPt0.0973 -3.229** -2.9511 1.2121 -3.045** -2.9484 I(1)

lnMPRt-3.088** – -2.9484 -3.034** – -2.9484 I(0)

lNFt-3.836** – -2.9484 -3.836** – -2.9484 I(0)

lnOILPt-1.1158 -5.659*** -2.9484 -1.1370 -5.656*** -2.9484 I(1)

TOPt-1.6204 -7.261*** -2.9484 -1.4882 -7.340*** -2.9484 I(1)

Note: The lag order (k) was selected using the Schwarz Bayesian information criterion. T-statistics or Critical values are reported. *, ** and *** denote rejection of the null hypothesis at Significant of 10%, 5% and 1% level, respectively for ADF and PP.

Table 4.2 shows the summary statistics of the unit root results and order of integration of the variables in the model. We observed that it was only inflation rate and monetary policy rate that was stationary and integrated at their level forms, that is, I(0). Whereas, real exchange rate, trade liberalization, economic growth and international crude oil price are not stationary but are integrated at order I(1). The summary statistics reveals that the variables in the model are not of the same order of integration, hence they are I(0) and I(1) series. The results necessitate the use of the bound cointegration technique for the test of any possible long run relationship among the variables in the model.

Table 4.3: Cointegration Test result

Critical Value Bounds

Significance Lower Limit Upper Limit

5% 3.23 4.35

1% 4.29 5.61

2.5% 3.69 4.89

Critical Value Number of Regressors

F-statistic 5.8576 3

The summary statistics on table 4.3 is the result of possible long-run relationship among the variables in the model. We used international crude oil prices and monetary policy rate as strictly exogenous variables in the model. The null hypothesis implies that there is no cointegration among the variables in the model. The F-statistic is reported to enable know the level of cointegration. The result shows that the value of the f-statistics is greater than the upper limit at 5%, implying a cointegrating relationship or long run relationship among the variables in the model.

Table4.4A: Model for Long-run determinants of Exchange Rate in Nigeria

Variable Coefficient Prob.

C -15.6893 0.1917

LOG(RGDP) 1.896295* 0.0956

LOG(INF) -1.276706* 0.0814

TOP 0.037815*** 0.0147

LOG(MPR) 1.911625** 0.0489

LOG(OILP) -0.395685 0.4473

R2 = 0.9958; Adjusted R2 = 0.9919 F-Stats =254.16***; DW-Stats = 2.3520

Table4.4B: Model for Short-run determinants of Exchange Rate in Nigeria

Variable Coefficient Prob.

DLOG(RGDP) -3.914297** 0.0146

DLOG(RGDP(-1)) 2.047722* 0.0605

D(INF) -0.218859*** 0.0043

D(INF) 0.382473*** 0.0004

D(INF) -0.108320 0.1328

D(TOP) 0.003653 0.3435

D(TOP(-1)) 0.001241 0.7016

D(TOP(-2)) -0.004489 0.2122

D(TOP(-3)) -0.009110** 0.0368

DLOG(MPR) 0.410557** 0.0294

DLOG(OILP) -0.084981 0.4507

ECM(-1) -0.614769** 0.0488

The model is estimated with the ARDL techniques. The lag lengths are selected automatically with Schwarz Information Criteria (SIC). Oil price and monetary rate are treated as strictly exogenous in the model; this is because oil price is determined by the Organization of petroleum Exporting Countries (OPEC) and MPR are determined by the Monetary Policy Committee (MPC) in at least six times a year. Hence their values are not affected by endogenous factors or not determined within the system.

The model has an R2 of 99.58 percent which is not 0.2 greater than the adjusted R2 of 99.19 percent, this implies that the model is an appropriate model for interpretation. Also, it was revealed that the overall model is statistically significant given the higher values of F-statistics that is significant at the 1% and that the residuals from the model is free from 1st order Markov scheme or autocorrelation.

Table4.5 Summary of Diagnostic Reports

Test/Hypothesis Tested(hypothesis are in null form) Test type Test-stats. Prob. Decision

Residual Normality

(Residuals are Normally Distributed) Jarque-Bera 0.2737 0.8735 Accept

Serial Correlation

(there is no serial correlation) Breusch-G LM Test

Q-Stat (lag length = 4) 1.2143

7.3456 0.4516

0.119 Accept

Accept

Heteroskedasticity

(there is Homoskedasticity) Harvey Test

Glejser Test 0.9639

1.0488 0.5262

0.4611 Accept

Accept

Functional form

(Model is Correctly Specified) Ramsey RESET Test 2.6858

0.1030 Accept

Table 4.5 shows the summary of diagnostic analysis of the second other test. The second order test of economic analysis states that the residual from the regression results should be normally distributed; the variance of the residual should be constant although the regression analysis; the residual should not be autocorrelated and that the model should be correctly specified for avoidance of specification bias. Results on table 4.5 show that the residual is normally distributed with a constant variance and are not serially correlated. Also the table reveals that the model is correctly specified since the null hypotheses were accepted in all.

4.3 Discussion of Findings

Real Exchange Rate and Economic Growth: economic growth as proxied as real GDP determined foreign exchange of the naira to a US dollar positively in the long-run. The result shows that this impact is elastic and insignificant at the 5%. The elastic impact implies that one percent increase in economic growth will lead to a more one percent depreciation of the naira to the US dollar. We reject the null hypothesis and concluded that economic growth is not a major determinant of the foreign exchange rate of the naira to a US dollar in the long-run. The short-run determinant of economic growth of the foreign exchange of the naira to a US dollar was negative and statistically significant at the 5%. This implies that a percentage increase in economic growth in the short-term will lead to more than one percent appreciation of the naira to the US dollar within the period of the review. Base on the above result we concluded that economic growth is a short-term determinant of foreign exchange rate in Nigeria.

Real Exchange rate and Inflation: inflation rate as proxied as domestic price stability determined foreign exchange of the naira to a US dollar negatively in the long-run. The result shows that this impact is elastic and insignificant at the 5%. The elastic impact implies that one percent increase in inflation will lead to a more one percent appreciation of the naira to the US dollar. We reject the null hypothesis and concluded that inflation is not a major determinant of the foreign exchange rate of the naira to a US dollar in the long-run. The short-run determinant of inflation of the foreign exchange of the naira to a US dollar was negative and statistically significant at the 5% in the current period but positive in the 1st lag period. This implies that a percentage increase in inflation in the very short-term will lead to more than one percent appreciation of the naira to the US dollar within the period of the review. The model revealed that previous values have inflation in Nigeria leads to the depreciation of the naira to a US dollar. Base on the above result we concluded that price stability is a short-term determinant of foreign exchange rate in Nigeria.

Foreign Exchange and Trade Liberalization: the result shows that trade liberalization, that is, openness of the economy determined foreign exchange of the naira to a US dollar positively in the long-run. The relationship is inelastic and statistically significant at the 5% implying that a percentage increased in the openness of the economy will lead to less than one percent depreciation of the naira to the US dollar in the long-run. The short-term coefficient shows that it will take at least three years for trade openness of the economy to affect foreign exchange rate in Nigeria, this is because it was only the third lag that was statistically significant at the 5%. Base on the above result we concluded trade liberalization determined of foreign exchange rate in Nigeria both in the short-term and the long-term. But the long-term impact is more pronounce than the short-term impact according to the model.

Foreign Exchange and Monetary Policy Rate: the result shows that MPR determined foreign exchange of the naira to a US dollar positively in the long-run. The relationship is elastic and statistically significant at the 5% implying that a percentage increased in the MPR will lead to more than one per cent depreciation of the naira to the US dollar in the long-run. The short-term coefficient shows that MPR affected foreign exchange rate in Nigeria positively and significantly. Base on the above result we concluded MPR determined foreign exchange rate in Nigeria both in the short-term and the long-term.

Foreign Exchange and Crude Oil Price: the result shows that OILP determined foreign exchange of the naira to a US dollar negatively in the long-run. The relationship is elastic and statistically insignificant at the 5% implying that a percentage increased in the OILP will lead to less than one percent appreciation of the naira to the US dollar in the long-run. The short-term coefficient shows that OILP affected foreign exchange rate in Nigeria negatively but insignificant. Base on the above result we concluded OILP is not a major determinant of foreign exchange rate in Nigeria both in the short-term and the long-term. Though the negative sign agrees to theoretical evidence and empirical evidence that pointed to the fact increased in oil price will lead to appreciation of oil exporting countries to the US dollar.

The short-term dynamics shows that it take up to a speed of 61.48 to correct the short-term disequilibrium as shown by the Error Correction Mechanism of the model.

CHAPTER FIVE

SUMMARY, CONCLUSION AND RECOMMENDATIONS

5.1 Summary of Work

We examine the determinants of foreign exchange rates in Nigeria within the periods of 1980 and 2016. The study particularly concentrated on the determinants of the exchange rate of the naira to the US dollar. The study examines selected macroeconomic variables and indicators that have affected the exchange rates in literatures as the major determinants of exchange rate in Nigeria. Five macroeconomic variables and indicators were selected as the determinants of exchange rate in Nigeria; they include economic growth as aggregate of the real sector performance, domestic price stabilities to account for domestic prices of good and service in the local economy, trade linearization to account for the degree of openness of the economy to trade, Monetary Policy Rate to account for monetary policy instrument and international crude oil price since oil account for over 80% of foreign exchange earning in Nigeria. The international crude oil price and Monetary Policy Rate were treated as strictly exogenous in the model because the both series are determined outside domestic economic activities. Oil price is determined by OPEC whereas Monetary Policy Rates is determined by the MPC. The model was estimated with the AutoRegressive Distributional Lag (ARDL) estimation technique. The technique was used because the variables in the model were of different order (I(0) and I(1)) of integration and cointegrated with the bound test method. The study is divided into five chapters.

Chapter one – Introduction is the introductory aspect of this research that seeks to give background knowledge of the past and present state of the subject matter.

Chapter two – Literature review attempts to investigate the findings of all other previous studies that have been carried out on the subject matter in order to see if there are certain existing gaps in the knowledge of the research topic.

Chapter three – Methodology attempts to suggest the types and sources of data that would be used in this research study and also how these data would be analyzed using varying econometric and statistical tools.

Chapter four – Discussion of results seeks to explain in the details the findings from the data obtained and analyzed while showing the existing economic relationships between the variables involved in this research.

Chapter five – Summary, Conclusion and Recommendations would give a summary of all findings of this research and would then attempt to make appropriate recommendations based on these findings.

5.1.2 Summary of Findings

Based on the results the following findings were summarized:

The variables in the model were of different order of integrations. There were I(0) and I(1) series and non were I(2). the proxies of exchange rate, economic growth, trade liberalization, and international crude oil price were I(1) while the proxies of domestic prices and monetary policy variable were I(0);

The variables in the model were highly cointegrated at all levels of significance. That is, there is a possible long-run relationship among the variables in the model; implying that any shocks in the short-run can be corrected in the long-run;

It was observed that economic growth is a short-run determinant of exchange rate in Nigeria. Although, increase in economic growth encourages domestic currency appreciation against the US dollar in the short-run;

Domestic prices is a short-run determinant of exchange rate in Nigeria, and it impact encourages domestic currency appreciations against the US dollar;

It was noticed that trade liberalization and monetary policy rate are the major determinants of the exchange rate. The both macroeconomic indicators determined the exchange rate of the naira to the US dollar in the long-run and in the short-term. The impact of the both series encourages the depreciation of the naira to the US dollar.

The model revealed that oil price is not a determinant of exchange rate in Nigeria.

The model was properly check and was also robust.

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APPENDIX

Null Hypothesis: LOG(RER) has a unit root Exogenous: Constant Lag Length: 0 (Automatic – based on SIC, maxlag=9)

t-Statistic Prob.*

Augmented Dickey-Fuller test statistic -1.934273 0.3134

Test critical values: 1% level -3.632900 5% level -2.948404 10% level -2.612874 *MacKinnon (1996) one-sided p-values. Augmented Dickey-Fuller Test Equation Dependent Variable: D(LOG(RER)) Method: Least Squares Date: 07/30/18 Time: 13:05 Sample (adjusted): 1982 2016 Included observations: 35 after adjustments Variable Coefficient Std. Error t-Statistic Prob.

LOG(RER(-1)) -0.049723 0.025707 -1.934273 0.0617

C 0.332869 0.096449 3.451245 0.0015

R-squared 0.101831 Mean dependent var 0.172275

Adjusted R-squared 0.074614 S.D. dependent var 0.301862

S.E. of regression 0.290382 Akaike info criterion 0.420205

Sum squared resid 2.782613 Schwarz criterion 0.509082

Log likelihood -5.353589 Hannan-Quinn criter. 0.450885

F-statistic 3.741410 Durbin-Watson stat 1.867424

Prob(F-statistic) 0.061692

Null Hypothesis: D(LOG(RER)) has a unit root Exogenous: Constant Lag Length: 0 (Automatic – based on SIC, maxlag=9)

t-Statistic Prob.*

Augmented Dickey-Fuller test statistic -5.022240 0.0002

Test critical values: 1% level -3.639407 5% level -2.951125 10% level -2.614300 *MacKinnon (1996) one-sided p-values. Augmented Dickey-Fuller Test Equation Dependent Variable: D(LOG(RER),2) Method: Least Squares Date: 07/30/18 Time: 13:05 Sample (adjusted): 1983 2016 Included observations: 34 after adjustments Variable Coefficient Std. Error t-Statistic Prob.

D(LOG(RER(-1))) -0.882281 0.175675 -5.022240 0.0000

C 0.154518 0.060731 2.544308 0.0160

R-squared 0.440783 Mean dependent var 0.005090

Adjusted R-squared 0.423308 S.D. dependent var 0.406516

S.E. of regression 0.308709 Akaike info criterion 0.544188

Sum squared resid 3.049643 Schwarz criterion 0.633974

Log likelihood -7.251201 Hannan-Quinn criter. 0.574808

F-statistic 25.22289 Durbin-Watson stat 1.993590

Prob(F-statistic) 0.000019

Null Hypothesis: INF has a unit root Exogenous: Constant Lag Length: 0 (Automatic – based on SIC, maxlag=9)

t-Statistic Prob.*

Augmented Dickey-Fuller test statistic -3.346479 0.0062

Test critical values: 1% level -3.632900 5% level -2.948404 10% level -2.612874 *MacKinnon (1996) one-sided p-values. Augmented Dickey-Fuller Test Equation Dependent Variable: D(INF) Method: Least Squares Date: 07/30/18 Time: 13:06 Sample (adjusted): 1982 2016 Included observations: 35 after adjustments Variable Coefficient Std. Error t-Statistic Prob.

INF(-1) -0.388239 0.137846 -2.816479 0.0081

C 7.507826 3.650097 2.056884 0.0477

R-squared 0.193796 Mean dependent var -0.146171

Adjusted R-squared 0.169365 S.D. dependent var 15.81789

S.E. of regression 14.41629 Akaike info criterion 8.230040

Sum squared resid 6858.374 Schwarz criterion 8.318917

Log likelihood -142.0257 Hannan-Quinn criter. 8.260721

F-statistic 7.932552 Durbin-Watson stat 1.600938

Prob(F-statistic) 0.008133

Null Hypothesis: INF has a unit root Exogenous: Constant Bandwidth: 5 (Newey-West automatic) using Bartlett kernel

Adj. t-Stat Prob.*

Phillips-Perron test statistic -3.689943 0.0059

Test critical values: 1% level -3.632900 5% level -2.948404 10% level -2.612874 *MacKinnon (1996) one-sided p-values. Residual variance (no correction) 195.9535

HAC corrected variance (Bartlett kernel) 169.8345

Phillips-Perron Test Equation Dependent Variable: D(INF) Method: Least Squares Date: 07/30/18 Time: 13:08 Sample (adjusted): 1982 2016 Included observations: 35 after adjustments Variable Coefficient Std. Error t-Statistic Prob.

INF(-1) -0.388239 0.137846 -2.816479 0.0081

C 7.507826 3.650097 2.056884 0.0477

R-squared 0.193796 Mean dependent var -0.146171

Adjusted R-squared 0.169365 S.D. dependent var 15.81789

S.E. of regression 14.41629 Akaike info criterion 8.230040

Sum squared resid 6858.374 Schwarz criterion 8.318917

Log likelihood -142.0257 Hannan-Quinn criter. 8.260721

F-statistic 7.932552 Durbin-Watson stat 1.600938

Prob(F-statistic) 0.008133

Null Hypothesis: MPR has a unit root Exogenous: Constant Lag Length: 0 (Automatic – based on SIC, maxlag=9)

t-Statistic Prob.*

Augmented Dickey-Fuller test statistic -3.087558 0.0368

Test critical values: 1% level -3.632900 5% level -2.948404 10% level -2.612874 *MacKinnon (1996) one-sided p-values. Augmented Dickey-Fuller Test Equation Dependent Variable: D(MPR) Method: Least Squares Date: 07/30/18 Time: 13:09 Sample (adjusted): 1982 2016 Included observations: 35 after adjustments Variable Coefficient Std. Error t-Statistic Prob.

MPR(-1) -0.400248 0.129632 -3.087558 0.0041

C 5.431108 1.765810 3.075704 0.0042

R-squared 0.224132 Mean dependent var 0.228571

Adjusted R-squared 0.200621 S.D. dependent var 3.494216

S.E. of regression 3.124109 Akaike info criterion 5.171620

Sum squared resid 322.0819 Schwarz criterion 5.260497

Log likelihood -88.50336 Hannan-Quinn criter. 5.202301

F-statistic 9.533017 Durbin-Watson stat 2.164607

Prob(F-statistic) 0.004072

Null Hypothesis: MPR has a unit root Exogenous: Constant Bandwidth: 3 (Newey-West automatic) using Bartlett kernel

Adj. t-Stat Prob.*

Phillips-Perron test statistic -3.034323 0.0414

Test critical values: 1% level -3.632900 5% level -2.948404 10% level -2.612874 *MacKinnon (1996) one-sided p-values. Residual variance (no correction) 9.202339

HAC corrected variance (Bartlett kernel) 8.452594

Phillips-Perron Test Equation Dependent Variable: D(MPR) Method: Least Squares Date: 07/30/18 Time: 13:09 Sample (adjusted): 1982 2016 Included observations: 35 after adjustments Variable Coefficient Std. Error t-Statistic Prob.

MPR(-1) -0.400248 0.129632 -3.087558 0.0041

C 5.431108 1.765810 3.075704 0.0042

R-squared 0.224132 Mean dependent var 0.228571

Adjusted R-squared 0.200621 S.D. dependent var 3.494216

S.E. of regression 3.124109 Akaike info criterion 5.171620

Sum squared resid 322.0819 Schwarz criterion 5.260497

Log likelihood -88.50336 Hannan-Quinn criter. 5.202301

F-statistic 9.533017 Durbin-Watson stat 2.164607

Prob(F-statistic) 0.004072 Null Hypothesis: TOP has a unit root Exogenous: Constant Lag Length: 0 (Automatic – based on SIC, maxlag=9)

t-Statistic Prob.*

Augmented Dickey-Fuller test statistic -1.620411 0.4618

Test critical values: 1% level -3.632900 5% level -2.948404 10% level -2.612874 *MacKinnon (1996) one-sided p-values. Augmented Dickey-Fuller Test Equation Dependent Variable: D(TOP) Method: Least Squares Date: 07/30/18 Time: 13:11 Sample (adjusted): 1982 2016 Included observations: 35 after adjustments Variable Coefficient Std. Error t-Statistic Prob.

TOP(-1) -0.166664 0.102853 -1.620411 0.1147

C 3.605497 3.016475 1.195268 0.2405

R-squared 0.073703 Mean dependent var -0.413557

Adjusted R-squared 0.045634 S.D. dependent var 10.39670

S.E. of regression 10.15671 Akaike info criterion 7.529591

Sum squared resid 3404.237 Schwarz criterion 7.618468

Log likelihood -129.7678 Hannan-Quinn criter. 7.560271

F-statistic 2.625731 Durbin-Watson stat 2.261235

Prob(F-statistic) 0.114662

Null Hypothesis: D(TOP) has a unit root Exogenous: Constant Lag Length: 0 (Automatic – based on SIC, maxlag=9)

t-Statistic Prob.*

Augmented Dickey-Fuller test statistic -7.260763 0.0000

Test critical values: 1% level -3.639407 5% level -2.951125 10% level -2.614300 *MacKinnon (1996) one-sided p-values. Augmented Dickey-Fuller Test Equation Dependent Variable: D(TOP,2) Method: Least Squares Date: 07/30/18 Time: 13:12 Sample (adjusted): 1983 2016 Included observations: 34 after adjustments Variable Coefficient Std. Error t-Statistic Prob.

D(TOP(-1)) -1.242524 0.171129 -7.260763 0.0000

C -0.403456 1.780614 -0.226582 0.8222

R-squared 0.622279 Mean dependent var 0.120102

Adjusted R-squared 0.610476 S.D. dependent var 16.62209

S.E. of regression 10.37416 Akaike info criterion 7.573536

Sum squared resid 3443.942 Schwarz criterion 7.663322

Log likelihood -126.7501 Hannan-Quinn criter. 7.604155

F-statistic 52.71868 Durbin-Watson stat 1.995721

Prob(F-statistic) 0.000000

Null Hypothesis: TOP has a unit root Exogenous: Constant Bandwidth: 3 (Newey-West automatic) using Bartlett kernel

Adj. t-Stat Prob.*

Phillips-Perron test statistic -1.488239 0.5277

Test critical values: 1% level -3.632900 5% level -2.948404 10% level -2.612874 *MacKinnon (1996) one-sided p-values. Residual variance (no correction) 97.26392

HAC corrected variance (Bartlett kernel) 84.10008

Phillips-Perron Test Equation Dependent Variable: D(TOP) Method: Least Squares Date: 07/30/18 Time: 13:12 Sample (adjusted): 1982 2016 Included observations: 35 after adjustments Variable Coefficient Std. Error t-Statistic Prob.

TOP(-1) -0.166664 0.102853 -1.620411 0.1147

C 3.605497 3.016475 1.195268 0.2405

R-squared 0.073703 Mean dependent var -0.413557

Adjusted R-squared 0.045634 S.D. dependent var 10.39670

S.E. of regression 10.15671 Akaike info criterion 7.529591

Sum squared resid 3404.237 Schwarz criterion 7.618468

Log likelihood -129.7678 Hannan-Quinn criter. 7.560271

F-statistic 2.625731 Durbin-Watson stat 2.261235

Prob(F-statistic) 0.114662

Null Hypothesis: D(TOP) has a unit root Exogenous: Constant Bandwidth: 6 (Newey-West automatic) using Bartlett kernel

Adj. t-Stat Prob.*

Phillips-Perron test statistic -7.339915 0.0000

Test critical values: 1% level -3.639407 5% level -2.951125 10% level -2.614300 *MacKinnon (1996) one-sided p-values. Residual variance (no correction) 101.2924

HAC corrected variance (Bartlett kernel) 92.66489

Phillips-Perron Test Equation Dependent Variable: D(TOP,2) Method: Least Squares Date: 07/30/18 Time: 13:13 Sample (adjusted): 1983 2016 Included observations: 34 after adjustments Variable Coefficient Std. Error t-Statistic Prob.

D(TOP(-1)) -1.242524 0.171129 -7.260763 0.0000

C -0.403456 1.780614 -0.226582 0.8222

R-squared 0.622279 Mean dependent var 0.120102

Adjusted R-squared 0.610476 S.D. dependent var 16.62209

S.E. of regression 10.37416 Akaike info criterion 7.573536

Sum squared resid 3443.942 Schwarz criterion 7.663322

Log likelihood -126.7501 Hannan-Quinn criter. 7.604155

F-statistic 52.71868 Durbin-Watson stat 1.995721

Prob(F-statistic) 0.000000

Null Hypothesis: LOG(RGDP) has a unit root Exogenous: Constant Lag Length: 1 (Automatic – based on SIC, maxlag=9)

t-Statistic Prob.*

Augmented Dickey-Fuller test statistic 0.097324 0.9609

Test critical values: 1% level -3.639407 5% level -2.951125 10% level -2.614300 *MacKinnon (1996) one-sided p-values. Augmented Dickey-Fuller Test Equation Dependent Variable: D(LOG(RGDP)) Method: Least Squares Date: 07/30/18 Time: 13:21 Sample (adjusted): 1983 2016 Included observations: 34 after adjustments Variable Coefficient Std. Error t-Statistic Prob.

LOG(RGDP(-1)) 0.001376 0.014143 0.097324 0.9231

D(LOG(RGDP(-1))) 0.502945 0.172824 2.910157 0.0066

C 0.008073 0.141235 0.057157 0.9548

R-squared 0.262058 Mean dependent var 0.044454

Adjusted R-squared 0.214449 S.D. dependent var 0.042327

S.E. of regression 0.037515 Akaike info criterion -3.644043

Sum squared resid 0.043629 Schwarz criterion -3.509364

Log likelihood 64.94874 Hannan-Quinn criter. -3.598114

F-statistic 5.504356 Durbin-Watson stat 1.887415

Prob(F-statistic) 0.009002

Null Hypothesis: D(LOG(RGDP)) has a unit root Exogenous: Constant Lag Length: 0 (Automatic – based on SIC, maxlag=9)

t-Statistic Prob.*

Augmented Dickey-Fuller test statistic -3.229346 0.0268

Test critical values: 1% level -3.639407 5% level -2.951125 10% level -2.614300 *MacKinnon (1996) one-sided p-values. Augmented Dickey-Fuller Test Equation Dependent Variable: D(LOG(RGDP),2) Method: Least Squares Date: 07/30/18 Time: 13:22 Sample (adjusted): 1983 2016 Included observations: 34 after adjustments Variable Coefficient Std. Error t-Statistic Prob.

D(LOG(RGDP(-1))) -0.489413 0.151552 -3.229346 0.0029

C 0.021788 0.009240 2.358020 0.0246

R-squared 0.245793 Mean dependent var 6.15E-05

Adjusted R-squared 0.222224 S.D. dependent var 0.041875

S.E. of regression 0.036930 Akaike info criterion -3.702561

Sum squared resid 0.043642 Schwarz criterion -3.612775

Log likelihood 64.94354 Hannan-Quinn criter. -3.671942

F-statistic 10.42868 Durbin-Watson stat 1.898813

Prob(F-statistic) 0.002867

Null Hypothesis: LOG(RGDP) has a unit root Exogenous: Constant Bandwidth: 2 (Newey-West automatic) using Bartlett kernel

Adj. t-Stat Prob.*

Phillips-Perron test statistic 1.212148 0.9976

Test critical values: 1% level -3.632900 5% level -2.948404 10% level -2.612874 *MacKinnon (1996) one-sided p-values. Residual variance (no correction) 0.001656

HAC corrected variance (Bartlett kernel) 0.002657

Phillips-Perron Test Equation Dependent Variable: D(LOG(RGDP)) Method: Least Squares Date: 07/30/18 Time: 13:22 Sample (adjusted): 1982 2016 Included observations: 35 after adjustments Variable Coefficient Std. Error t-Statistic Prob.

LOG(RGDP(-1)) 0.023187 0.013825 1.677166 0.1030

C -0.193708 0.141116 -1.372687 0.1791

R-squared 0.078544 Mean dependent var 0.042668

Adjusted R-squared 0.050621 S.D. dependent var 0.043018

S.E. of regression 0.041915 Akaike info criterion -3.450913

Sum squared resid 0.057976 Schwarz criterion -3.362036

Log likelihood 62.39098 Hannan-Quinn criter. -3.420233

F-statistic 2.812884 Durbin-Watson stat 1.022439

Prob(F-statistic) 0.102959 Null Hypothesis: D(LOG(RGDP)) has a unit root Exogenous: Constant Bandwidth: 3 (Newey-West automatic) using Bartlett kernel

Adj. t-Stat Prob.*

Phillips-Perron test statistic -3.044705 0.0407

Test critical values: 1% level -3.639407 5% level -2.951125 10% level -2.614300 *MacKinnon (1996) one-sided p-values. Residual variance (no correction) 0.001284

HAC corrected variance (Bartlett kernel) 0.001017

Phillips-Perron Test Equation Dependent Variable: D(LOG(RGDP),2) Method: Least Squares Date: 07/30/18 Time: 13:22 Sample (adjusted): 1983 2016 Included observations: 34 after adjustments Variable Coefficient Std. Error t-Statistic Prob.

D(LOG(RGDP(-1))) -0.489413 0.151552 -3.229346 0.0029

C 0.021788 0.009240 2.358020 0.0246

R-squared 0.245793 Mean dependent var 6.15E-05

Adjusted R-squared 0.222224 S.D. dependent var 0.041875

S.E. of regression 0.036930 Akaike info criterion -3.702561

Sum squared resid 0.043642 Schwarz criterion -3.612775

Log likelihood 64.94354 Hannan-Quinn criter. -3.671942

F-statistic 10.42868 Durbin-Watson stat 1.898813

Prob(F-statistic) 0.002867

ARDL Cointegrating And Long Run Form Dependent Variable: LOG(RER) Selected Model: ARDL(1, 2, 3, 4) Date: 07/30/18 Time: 13:23 Sample: 1981 2016 Included observations: 32 Cointegrating Form

Variable Coefficient Std. Error t-Statistic Prob.

DLOG(RGDP) 0.021788 1.429835 0.015238 0.9880

DLOG(RGDP(-1)) 2.047722 1.014018 2.019414 0.0605

D(INF) -0.218859 0.065922 -3.319971 0.0043

D(INF) 0.382473 0.085649 4.465569 0.0004

D(INF) -0.108320 0.068398 -1.583682 0.1328

D(TOP) 0.003653 0.003742 0.976214 0.3435

D(TOP(-1)) 0.001241 0.003180 0.390119 0.7016

D(TOP(-2)) -0.004489 0.003455 -1.299331 0.2122

D(TOP(-3)) -0.009110 0.004000 -2.277369 0.0368

DLOG(MPR) 0.410557 0.171625 2.392181 0.0294

DLOG(OILP) -0.084981 0.109917 -0.773137 0.4507

CointEq(-1) -1.489413 0.100691 -14.791950 0.0000

Cointeq = LOG(RER) – (-1.3548*LOG(RGDP) -0.1841*LOG(INF) + 0.0055

*TOP + 0.2757*LOG(MPR) -0.0571*LOG(OILP) -2.2623 )

Long Run Coefficients

Variable Coefficient Std. Error t-Statistic Prob.

LOG(RGDP) -1.354803 0.356699 -3.798171 0.0016

LOG(INF) -0.184097 0.057124 -3.222740 0.0053

TOP 0.005453 0.003735 1.459849 0.1637

LOG(MPR) 0.275650 0.108233 2.546830 0.0215

LOG(OILP) -0.057057 0.073117 -0.780345 0.4466

C -2.262350 2.500484 -0.904765 0.3790

Dependent Variable: LOG(RER) Method: ARDL Date: 07/02/18 Time: 09:01 Sample (adjusted): 1985 2016 Included observations: 32 after adjustments Maximum dependent lags: 4 (Automatic selection)

Model selection method: Schwarz criterion (SIC)

Dynamic regressors (4 lags, automatic): LOG(RGDP) LOG(INF) TOP

Fixed regressors: LOG(MPR) LOG(OILP) C Number of models evalulated: 500 Selected Model: ARDL(1, 2, 3, 4) Variable Coefficient Std. Error t-Statistic Prob.*

LOG(RER(-1)) 0.785231 0.100691 7.798443 0.0000

LOG(RGDP) -3.914297 1.429835 -2.737586 0.0146

LOG(RGDP(-1)) 6.369284 1.958791 3.251641 0.0050

LOG(RGDP(-2)) -2.047722 1.014018 -2.019414 0.0605

LOG(INF) -0.218859 0.065922 -3.319971 0.0043

LOG(INF(-1)) 0.218816 0.088052 2.485061 0.0244

LOG(INF(-2)) -0.382473 0.085649 -4.465569 0.0004

LOG(INF(-3)) 0.108320 0.068398 1.583682 0.1328

TOP 0.003653 0.003742 0.976214 0.3435

TOP(-1) -0.007890 0.003544 -2.226199 0.0407

TOP(-2) -0.001241 0.003180 -0.390119 0.7016

TOP(-3) 0.004489 0.003455 1.299331 0.2122

TOP(-4) 0.009110 0.004000 2.277369 0.0368

LOG(MPR) 0.410557 0.171625 2.392181 0.0294

LOG(OILP) -0.084981 0.109917 -0.773137 0.4507

C -3.369572 3.941742 -0.854843 0.4053

R-squared 0.995821 Mean dependent var 3.751791

Adjusted R-squared 0.991903 S.D. dependent var 1.527431

S.E. of regression 0.137446 Akaike info criterion -0.824316

Sum squared resid 0.302263 Schwarz criterion -0.091448

Log likelihood 29.18906 Hannan-Quinn criter. -0.581391

F-statistic 254.1613 Durbin-Watson stat 3.237737

Prob(F-statistic) 0.000000 *Note: p-values and any subsequent tests do not account for model

selection.

ARDL Bounds Test Date: 07/02/18 Time: 08:58 Sample: 1985 2016 Included observations: 32 Null Hypothesis: No long-run relationships exist

Test Statistic Value k F-statistic 5.857598 3 Critical Value Bounds Significance I0 Bound I1 Bound 10% 2.72 3.77 5% 3.23 4.35 2.5% 3.69 4.89 1% 4.29 5.61 Test Equation: Dependent Variable: DLOG(RER) Method: Least Squares Date: 07/02/18 Time: 08:58 Sample: 1985 2016 Included observations: 32 Variable Coefficient Std. Error t-Statistic Prob.

DLOG(RGDP) -1.010143 1.581447 -0.638746 0.5310

DLOG(RGDP(-1)) -0.429258 1.235034 -0.347568 0.7322

D(INF) 0.003773 0.002718 1.388434 0.1819

D(TOP) -9.39E-05 0.004347 -0.021604 0.9830

D(TOP(-1)) -0.021101 0.005103 -4.135112 0.0006

D(TOP(-2)) -0.020405 0.005514 -3.700688 0.0016

D(TOP(-3)) -0.019007 0.004722 -4.025585 0.0008

LOG(MPR) 0.816539 0.201302 4.056292 0.0007

LOG(OILP) -0.139196 0.154009 -0.903817 0.3780

C -10.77009 4.548208 -2.367985 0.0293

LOG(RGDP(-1)) 1.016697 0.468058 2.172161 0.0434

INF -0.005754 0.002684 -2.144179 0.0459

TOP(-1) 0.013916 0.006457 2.155228 0.0449

LOG(RER(-1)) -0.356693 0.115991 -3.075189 0.0065

R-squared 0.773896 Mean dependent var 0.181354

Adjusted R-squared 0.610598 S.D. dependent var 0.314508

S.E. of regression 0.196260 Akaike info criterion -0.119122

Sum squared resid 0.693321 Schwarz criterion 0.522138

Log likelihood 15.90594 Hannan-Quinn criter. 0.093438

F-statistic 4.739172 Durbin-Watson stat 2.315979

Prob(F-statistic) 0.001428

Ramsey RESET Test Equation: UNTITLED Specification: LOG(RER) LOG(RER(-1)) LOG(RGDP) LOG(RGDP(-1))

LOG(RGDP(-2)) LOG(INF) LOG(INF(-1)) LOG(INF(-2)) LOG(INF(-3))

TOP TOP(-1) TOP(-2) TOP(-3) TOP(-4) LOG(MPR) LOG(OILP) C

Omitted Variables: Powers of fitted values from 2 to 3

Value df Probability F-statistic 2.685760 (2, 14) 0.1030 F-test summary: Sum of Sq. df Mean Squares Test SSR 0.083814 2 0.041907 Restricted SSR 0.302263 16 0.018891 Unrestricted SSR 0.218449 14 0.015603 Unrestricted Test Equation: Dependent Variable: LOG(RER) Method: ARDL Date: 07/02/18 Time: 09:00 Sample: 1985 2016 Included observations: 32 Maximum dependent lags: 4 (Automatic selection)

Model selection method: Schwarz criterion (SIC)

Dynamic regressors (4 lags, automatic): Fixed regressors: C Variable Coefficient Std. Error t-Statistic Prob.*

LOG(RER(-1)) 0.684591 0.192261 3.560741 0.0031

LOG(RGDP) -3.139715 1.416949 -2.215828 0.0438

LOG(RGDP(-1)) 6.103057 1.886956 3.234341 0.0060

LOG(RGDP(-2)) -2.140048 0.929216 -2.303069 0.0371

LOG(INF) -0.247458 0.075757 -3.266470 0.0056

LOG(INF(-1)) 0.187543 0.083017 2.259103 0.0404

LOG(INF(-2)) -0.398179 0.095616 -4.164367 0.0010

LOG(INF(-3)) 0.032015 0.070386 0.454853 0.6562

TOP 0.005571 0.003639 1.530764 0.1481

TOP(-1) -0.006436 0.003628 -1.773623 0.0979

TOP(-2) -0.002917 0.002986 -0.976756 0.3453

TOP(-3) 0.001233 0.003877 0.317986 0.7552

TOP(-4) 0.010472 0.006216 1.684807 0.1142

LOG(MPR) 0.318188 0.252934 1.257992 0.2290

LOG(OILP) -0.212119 0.114188 -1.857620 0.0844

C -6.516848 4.812779 -1.354072 0.1972

FITTED^2 0.106986 0.126864 0.843316 0.4132

FITTED^3 -0.019283 0.015901 -1.212731 0.2453

R-squared 0.996980 Mean dependent var 3.751791

Adjusted R-squared 0.993312 S.D. dependent var 1.527431

S.E. of regression 0.124914 Akaike info criterion -1.024063

Sum squared resid 0.218449 Schwarz criterion -0.199586

Log likelihood 34.38501 Hannan-Quinn criter. -0.750772

F-statistic 271.8320 Durbin-Watson stat 3.049869

Prob(F-statistic) 0.000000 *Note: p-values and any subsequent tests do not account for model

selection.

Breusch-Godfrey Serial Correlation LM Test: F-statistic 9.343464 Prob. F(2,14) 0.0026

Obs*R-squared 18.29422 Prob. Chi-Square(2) 0.0001

Test Equation: Dependent Variable: RESID Method: ARDL Date: 07/02/18 Time: 09:01 Sample: 1985 2016 Included observations: 32 Presample missing value lagged residuals set to zero.

Variable Coefficient Std. Error t-Statistic Prob.

LOG(RER(-1)) 0.061312 0.072134 0.849974 0.4096

LOG(RGDP) 0.273909 1.053909 0.259898 0.7987

LOG(RGDP(-1)) -0.789095 1.443271 -0.546741 0.5932

LOG(RGDP(-2)) 0.209353 0.722460 0.289778 0.7762

LOG(INF) -0.002922 0.049460 -0.059077 0.9537

LOG(INF(-1)) -0.029660 0.068764 -0.431325 0.6728

LOG(INF(-2)) 0.026250 0.066334 0.395722 0.6983

LOG(INF(-3)) 0.041700 0.048912 0.852545 0.4083

TOP -0.001618 0.002795 -0.579013 0.5718

TOP(-1) -0.002431 0.002593 -0.937843 0.3642

TOP(-2) -0.000338 0.002227 -0.151715 0.8816

TOP(-3) -0.000183 0.002480 -0.073747 0.9423

TOP(-4) 0.000469 0.002961 0.158259 0.8765

LOG(MPR) 0.180345 0.137021 1.316184 0.2093

LOG(OILP) 0.101646 0.081018 1.254615 0.2302

C 2.101872 2.848003 0.738016 0.4727

RESID(-1) -1.068384 0.281217 -3.799146 0.0020

RESID(-2) -0.362084 0.290661 -1.245725 0.2333

R-squared 0.571694 Mean dependent var 1.23E-14

Adjusted R-squared 0.051609 S.D. dependent var 0.098744

S.E. of regression 0.096162 Akaike info criterion -1.547234

Sum squared resid 0.129461 Schwarz criterion -0.722758

Log likelihood 42.75575 Hannan-Quinn criter. -1.273944

F-statistic 1.099231 Durbin-Watson stat 1.815175

Prob(F-statistic) 0.434286

Heteroskedasticity Test: Breusch-Pagan-Godfrey

F-statistic 1.191710 Prob. F(15,16) 0.3650

Obs*R-squared 16.88590 Prob. Chi-Square(15) 0.3257

Scaled explained SS 3.680493 Prob. Chi-Square(15) 0.9986

Test Equation: Dependent Variable: RESID^2 Method: Least Squares Date: 07/02/18 Time: 08:59 Sample: 1985 2016 Included observations: 32 Variable Coefficient Std. Error t-Statistic Prob.

C -0.308938 0.347663 -0.888614 0.3874

LOG(RER(-1)) -0.009525 0.008881 -1.072470 0.2994

LOG(RGDP) -0.081684 0.126112 -0.647715 0.5264

LOG(RGDP(-1)) 0.098348 0.172766 0.569255 0.5771

LOG(RGDP(-2)) 0.009699 0.089437 0.108441 0.9150

LOG(INF) -0.001347 0.005814 -0.231739 0.8197

LOG(INF(-1)) 0.006531 0.007766 0.840962 0.4128

LOG(INF(-2)) -0.004427 0.007554 -0.586058 0.5660

LOG(INF(-3)) 0.002433 0.006033 0.403309 0.6921

TOP -0.000138 0.000330 -0.417456 0.6819

TOP(-1) -0.000326 0.000313 -1.043040 0.3124

TOP(-2) -6.58E-05 0.000280 -0.234611 0.8175

TOP(-3) 0.000454 0.000305 1.490862 0.1554

TOP(-4) 0.000595 0.000353 1.686295 0.1111

LOG(MPR) 0.018181 0.015137 1.201060 0.2472

LOG(OILP) 0.005035 0.009695 0.519396 0.6106

R-squared 0.527684 Mean dependent var 0.009446

Adjusted R-squared 0.084888 S.D. dependent var 0.012673

S.E. of regression 0.012123 Akaike info criterion -5.680608

Sum squared resid 0.002351 Schwarz criterion -4.947740

Log likelihood 106.8897 Hannan-Quinn criter. -5.437683

F-statistic 1.191710 Durbin-Watson stat 1.354989

Prob(F-statistic) 0.364982