Impact of Exchange Rate on Economic Growth

Fixed Exchange Rate and Economic Growth

A fixed exchange rate is a country’s exchange rate regime under which the government or central bank ties the official exchange rate to another country’s currency or to the price of gold. The purpose of a fixed exchange rate system is to maintain a country’s currency value within a very narrow band. Fixed rates provide greater certainty for exporters and importers, which also help the government, maintain low inflation which in the long run will tend to keep interest rates down and stimulate increased trade and investment. Most major industrialized nations have had floating exchange rate systems since the early 1970s, while developing economies continue to have fixed rate systems.

Developing economies often utilize a fixed rate system to limit speculation and provide a stable system to allow importers, exporters and investors to plan without worrying about currency moves. However, a fixed rate system limits a central bank’s ability to adjust interest rates as needed for economic growth. It also prevents market adjustments when a currency becomes over- or undervalued. Effective management of such a system also requires a large pool of reserves to support the currency when it is under pressure.

Exchange rate flexibility and the real economy

The extent of exchange rate flexibility, operating through a number of channels, has implications for both the real economy’s long-run growth prospects and its volatility. This section outlines the trends in exchange rate flexibility in our sample of emerging economies in the past decade, discusses the relevant channels through which they influence real activity and documents their importance.

Exchange rate flexibility and long-term growth

Exchange rate flexibility could affect long-run economic growth if it has an impact on productivity growth. Both the level and volatility of the exchange rate are at play here.

With respect to the level, the early literature argues in favour of an undervalued exchange rate for the promotion of domestic industries. Many emerging economies continue to have growth models heavily reliant on exports (BIS (2012)). Rodrik (2008) shows in a theoretical model how exchange rate undervaluation can stimulate growth if the tradable goods sector is affected disproportionately by market failures or institutional weaknesses. In addition, trend appreciations and depreciations can have negative implications for foreign direct investment through the location of industries. These considerations suggest that limiting exchange rate flexibility could matter, especially for the tradable goods sector. Large and frequent changes in the exchange rate can create a volatile economic structure, particularly if financial markets are underdeveloped and agents have few hedging possibilities. Such a volatile economy could adversely affect prospects for investment and growth. It could also reduce international trade, especially in economies dependent on intra-regional trade because large exchange rate changes have compounding effects on the costs of intermediate inputs (see egThorbecke (2008)). But greater exchange rate flexibility could also lead to a more efficient allocation of resources and higher growth. It could encourage innovation and productivity growth, as domestic firms cannot rely on undervalued exchange rates and FX intervention to maintain external competitiveness. When exchange rates are flexible and financial markets are well developed, investment and production decisions can be disconnected from movements in the exchange rate. Capturing the long-run impact of the exchange rate on growth is difficult because of the lack of information on total factor productivity in many EMEs. In general, econometric analysis gives inconclusive evidence about the relationship between exchange rate volatility and long-run growth. We regressed labour productivity growth during 2000–11 on real exchange rate volatility during the same period, and on the level of initial income observed in 1999.5 For a pooled sample comprising 52 advanced and emerging economies, the cross-sectional estimation yields a statistically insignificant and negative coefficient on exchange rate volatility, while the initial level of income appears as an important determinant of productivity growth, with a negative and statistically significant coefficient. This is in line with convergence effects in standard growth regressions. Chow breakpoint tests that we subsequently carried out were not able to establish a threshold level of initial income above or below which exchange rate volatility would become a statistically significant determinant of growth

Exchange rate flexibility and output volatility

Moving from the level of growth to its volatility, a more flexible exchange rate could protect the economy against the adverse impacts of external shocks through its countercyclical role in reducing output volatility (egObstfeld and Rogoff (1995)).9 Graph 2 suggests that there is a U-shaped relationship between real exchange rate volatility and output volatility in emerging economies (left-hand panel), when output volatility is measured by the standard deviation of quarterly real GDP growth. Up to a point, increased flexibility of the real exchange rate acts as a shock absorber and helps toinsulate the economy against shocks. But extreme exchange rate flexibility can itself become a source of real volatility. This can arise if exchange rates display overshooting behaviour10 and thereby become sources of shocks themselves; if a large exchange rate movement reflects a sudden stop of capital flows and a balance of payments crisis; or if large exchange rate movements exacerbate the impact of structural vulnerabilities in the economy, such as currency mismatches

Devaluation and Economic Growth

Often a devaluation (fall in the value of the exchange rate) can cause a boost to economic growth. A lower exchange rate makes exports cheaper and increases demand for Pakistani goods. This can provide additional demand which increases economic growth.

• Devaluation may cause inflation:
• Imports more expensive
• Higher domestic demand

But, if demand for exports and imports is relatively elastic and there is some spare capacity in the economy, then there should be an increase in economic growth.

Objectives of the study

The main objectives of this study are to determine

1. To analyze the exchange rate depreciation impact on economic growth and integrally both in fixed and floating exchange rate regimes, since the fixed exchange rate is the most dominant factor in the Pakistan economy and depreciation remains a common phenomenon in both fixed and floating exchange regimes.
2. Relationship between exchange rate and economic growth of Pakistan.

Hypotheses

From the above literature following hypothesis are developed:-

H0: There is no relationship between exchange rates and Economic growth.

H1: There is a relationship between exchange rates and Economic growth.

CHAPTER 2

LITERATURE REVIEW

Sabina et al (2017). Investigated exchange rate volatility in Nigeria and its effect on economic growth. They used a data over the period of 1981 to 2015.And they used GARCH model.  Result showed that volatility and FDI has negative and significant impact on the growth of the Nigerian economy.

Bagh et al(2017).The exchange rate Volatility was considered to be the most important and persuasive variable that affects the performance of stock index.They used a data over the period of 2003 to 2015.they used ADF model foe estimation. results found that there is positive and statistically significant relationship between Exchange Rate Volatility on Stock Index of Pakistan.

SU and Wu (2017) analyzed the relationship between the exchange rate and the real GDP of China in the long period .They used a data over the period of 1952 to 2014.And they used  a structural VAR model. The results show that there is no obvious relationship between the exchange rate and real GDP before China’s reform and opening-up period.

Maiga (2017).The Nigerian economy faced numerous challenges which impacted the overall economic activity and has witnessed crises with devastating consequences on the world commodity prices as a result of global economics.He used adata over the period of 1990 to 2013.And he used OLS model for estimation. . The result found that the interest rate has a slight impact on growth; however the growth can be improved by lower the interest rate which will increase the investment.

Aslam (2016) identified that Exchange rate as a factor for turning vector of the economic growth of countries which was empirically confirmed by several related studies. He used annual time series data from 1970 to 2015 and the variables such as gross domestic product, exchange rate, inflation rate, and interest rate were considered and the multiple regressions model using Ordinary Least squared method was employed. Finally the study confirmed that the exchange rate positively influenced on the economic growth in Sri Lanka at one percent significant level.

Jakob (2016)  Identifieda direct correlation between exchange rate regimes and economic growth. One of the most important issues left unanswered in international finance is the debates over which type of exchange rate can best stimulate economic growth.He used a ANOVA test. The data from 74 countries for year 2012, it is found that there is a positive and significant correlation between pegged exchange rate and growth in GDP.

Akpan(2016) investigated the effect of exchange rate movements on real output growth in Nigeria. This paper examines the possible direct and indirect relationship between exchange rates and GDP growth .They used a data over a period of 1986 to 2010.And they used ADF test for estimation. The estimation results suggest that there is no evidence of a strong direct relationship between changes in exchange rate and output growth. Rather, Nigeria’s economic growth has been directly affected by monetary variables.

Anwar et al (2016) conducted the monetary policy to minimize the economic fluctuations. Monetary policy effects the financial and economic decisions of the people. They used a data over the period of 1962 to 2011.And they used ADFtest.And The Ordinary Least Squares technique is used to evaluate the impact of monetary policy on economic growth. It is also concluded that the monetary authorities of Pakistan cannot control money supply changes although they are able to influence such changes.

Akongi (2016).Investidatedtaht exchange rate volatility, and what are the effects of excessive fluctuations in the exchange rate on economic growth in Ghana? They used a data over the period of 1980 to 2010.And they used GARCH (General Autoregressive Conditional Heteroscedasticity) and ARCH Model. The results showed that while shocks to the exchange rate are mean reverting, misalignments tend to correct very sluggishly, with painful consequences in the short run as economic agents recalibrate their consumption and investment choices.

Kamal Uddin (2014). Examined the relationship between Exchange Rate (ER) and Economic Growth (EG) proxied by Real Gross Domestic Product (RGDP) in Bangladesh for a period of 41 years ranges from 1973 to 2013.They used a unit root,causality and co-integration model foe estimation. The empirical results show that there is a significant positive correlation between ER and EG. The results also advocate the presence of long-run equilibrium relationship between ER and EG.

Adeniranetal (2014) examined the impact of exchange rate on economic growth from 1986 to 2013.they used a OLS model for estimation.The result revealed that exchange rate has positive impact.The results also indicated that interest rate and rate of inflation have negatively impact on economic growth.

Perpetua (2014). examined the impact of exchange rate variation and inflation on the economic growth of Nigeria .Ordinary least square (OLS) was used to analyze the time series data. He used a data over the period of 1980 to 2010. This implies a positive relationship between inflation and exchange rate. Base on strength of our findings, the researcher submits that macroeconomic policies aimed at enhancing sustainable economic development should not over concentrate at fighting inflation but should on other area of economic development such as factor input productivity and human capital development.

AzeemNaseer(2013) aim to do an empirical investigation of the causal relationship among, trade, real effective exchange rates and economic growth .He used a data over a period of 1980 to 2012.He used a Johansson co-integration model. The results of ECM suggest that there is a significant relationship between the exchange rate and economic growth.

Tang (2013) examined the impact of intra-Asia exchange rate volatility on intra-Asia trade in primary goods, intermediate goods, equipment goods and consumption goods from 1980 to 2009. Showd that as intraregional exchange rate volatility increases, intraregional exports in these goods fall for Asian while the impact is more pronounced in the sub region of Association of Southeast Asian Nations (ASEAN) and other ASEAN member Again, the impact magnifies in an even smaller subgroup excluding the smaller ASEAN economies. For South Asia, exchange rate volatility appears to have a positive impact on exports. The results according to Him underline the significant impact of exchange rate volatility on the region’s production networks.

Bala and Asemota (2013)Studied the relationship between the exchange rate and economic growth using the Bangladesh data from the period of 1981 to 2013. To test this relationship in this study, the exchange rate and the export income was considered as independent variable and the gross domestic product in constant price was deemed as dependent variable and the multiple regressions model was employed. This study concluded that the exchange rate positively impacted on the economic growth of Bangladesh during the sample periods.

Ahmed et al.(2013) examined that Depreciation remained a common factor in Pakistani economic history in different regimes, which affected different economic variables, especially the growth and business sector They used a data over the period of 1976 to 2010.And they used ADF unit root test for estimation. All these findings reveal that depreciation is not a good practice because it has negative impact for growth in the business sector. The present scenario of the flexible exchange rate doesn’t allow the corresponding authorities to set desirable exchange rate , however, the government must reinforce the real sector in order to ensure a stable exchange rate and hence macroeconomic stability.

Saqib and sana (2012) analyzed the effect of exchange rate volatility on the trade volume (Export) and the impact of REER (Real effective exchange rate) on determinant of trade in Pakistan using time series data for the period 1981-2010.They used ADF and Phillips perron test. . The result shows that REER (Real effective exchange rate) is inversely impact on Export Volume of Pakistan. On other side the Import is directly correlated with the volume of export, and positively impacts our Export, in the case of developing nation, the impact of import is also directly correlated with the volume of export.

Pokhariyal  et al. (2012) examined the impact of real exchange rate volatility on economic growth in Kenyan. They used the computation of the unconditional standard deviation  of changes to measure volatility and Generalized Method Moments (GMM) to assess the impact of the real exchange rate volatility on economic growth with data spanning from 1993- 2009. The study found that Real Exchange Rate (RER) was very volatility for the entire study period. Kenya‟s RER generally exhibited a appreciating and volatility trend. The RER Volatility reflected a negative impact on economic growth of Kenya.

Vieira and MacDonald (2012) investigated the role of real exchange rate on long-run growth for a set of ninety countries using time series data from 1980 to 2004. An estimate panel data model (using fixed and random effects) and panel cointegration methods for the real exchange rate were employed. The variables used in real exchange rate models are: real per capita GDP; net foreign assets; terms of trade and government consumption. The results for the two-step System GMM panel growth models indicate that the coefficients for real exchange rate misalignment are positive for different model specification and samples, which means that a more preciated (appreciated) real exchange rate helps (harms) long-run growth.

Anthony (2011).The effects of exchange rate volatility on economic growth have over the years been an issue for both policy makers and academicians on the efficiency of alternative exchange rate policies. He used a data over the period of 1983 to 2010.And used a GARCH (General Autoregressive Conditional Heteroscedasticity Model). The results indicated that human development index with gross domestic investment, technology; exchange rate volatility explains growth in Ghana much better than when human development index is proxied by either labor force or population.

Ping HUA (2011) investigated that the Real exchange rate exert different economic and social effects. An econometric model is estimated by using the GMM system estimation approach and panel data for the 29 Chinese provinces and over the period from 1987 to 2008.The results shows that the real exchange rate appreciation had a negative effect on the economic growth, higher in coastal than in inland provinces, contributing to a minimizing of the gap of GDP per capita between two kinds of the provinces. They show moreover that the real exchange rate appreciation acted negative effects on employment.

.  Abeaze (2011)  Investigated the effect of exchange rate on macroeconomic performance in Nigeria using annual time series data during the period of 1986 to 2010. In this study, the multiple regressions model was used and this study found that the exchange rate positively impacted on the economic growth.

Holland  et al. (2011)  assessed the role of real effective exchange rate volatility on long-run economic growth for a set of 82 advanced and emerging economies using a panel data set ranging from 1970 to 2009. The results for the two-step system GMM panel growth models show that a more volatile RER has significant negative impact on economic growth and the results are robust for different model specifications.

Mukhtar and Malik (2010) investigated the the impact of exchange rate volatility on growth of three South Asian countries, India, Pakistan and Sri Lanka Using cointegration and vector error correction model (VECM) techniques for the period 1960 to 2007. Findings indicated the presence of a unique cointegrating vector linking real exports, relative export prices, foreign economic activity and real exchange rate volatility in the long run. Real exchange rate volatility exerts significant negative effects on exports both in the short run and the long run.  Results also reveal that improvements in the terms of trade (represented by declines in the real exchange rate) and real foreign income exert positive effects on export activity.

Etim  et al. (2009) examined the determinants of exchange rate instability (volatility of Real Exchange Rate) in a developing economy. The study used ordinary least square (OLS) technique in relation to time series data on exchange rate instability (VRER), current account balance (CABY), import (IMPY), External Reserves (EXTRESS), inflation and economic growth (GDP). Instability of exchange rate is measured by three years moving average of standard deviation of real exchange rate. The paper advocates that a realistic exchange rate capable of accelerating economic growth, reducing import and also stemming the tide of inflation are paramount in a developing economy and should be maintained.

Rodrick(2008) showed that undervaluation of the currency (a high real exchange rate) stimulates economic growth. This is true particularly for developing countries.He used a data over the period of 1950 t0 2005.And he used a unit root test. The results suggest that tradables suffer disproportionately from the government or market failures that keep poor countries from converging toward countries with higher incomes

Ashour and chen (2007) showed that An increment in the quantity of goods or services manufactured per head of the population over time denotes economic growth of a country. They used a data over the period of 1974 to 2006.Analysis of data was performed through SPSS.The results indicated that as compared to flexible exchange regime was adopted and higher  by 1.2%. When fixed exchange regime was adopted and a growth rate of 0.64% was achieved under the intermediate regime when compared with a flexible regime.

Poon  et al. (2005) examined the relationship between exchange rate volatility and exports of the five selected East Asian economies. They used  a data over the period of 1985 to 2000. Vector autoregressive (VAR) model, error correction modelling (ECM), and variance decomposition (VD) are applied to characterize the joint dynamics of variables in both the short and long run. Results further show that a great fluctuation of exchange rate volatility has significantly impacted the volume of exports for the economies concerned. The forecast error VD shows that the innovations of exchange rate volatility have minor impact on export patterns in the study.

Kandil (2004) examined the effects of exchange rate fluctuations on real output growth and price inflation in a sample of twenty-two developing countries. The analysis introduces a theoretical rational expectation model that decomposes movements in the exchange rate into anticipated and unanticipated components.He used a data over the period of 1955 to 1995.And he used a ARDL model. The evidence confirms concerns about the negative effects of currency depreciation on economic performance in developing countries.

Hau (2002) researched on the openness of an economy and its impact on real exchange rate movements using a small open economy model with a tradable and a non-tradable sector with a sample of 48 countries over a 19-year time period. The results confirm the impact of an economy‟s openness on exchange rate volatility when openness explains almost half of exchange rate variations. He claims that trade integration and real exchange rate volatility is structurally linked and that there is a negative correlation between them.

Brodsky (1984) investigated the impact of exchange rate stability on growth for a sample of 41 mostly small open economies at the EMU periphery. Panel estimations reveal a robust negative relationship between exchange rate volatility and growth for countries in the economic catch-up process with open capital accounts It is argued that fixed exchange rates provide a more stable framework for the adjustment of asset and labor markets of countries in the economic catch-up process thereby accelerating growth.

CHAPTER 3

THEORETICAL FRAMEWORK AND METHODOLOGY

The exchange rate (ER) represents the number of units of one currency that exchanges for a unit of another. There are two ways to express an exchange rate between two currencies (e.g., between the U.S. dollar [$] and the British pound [£]). One can either write $/£ or £/$. These are reciprocals of each other. Thus if E is the $/£ exchange rate and V is the £/$ exchange rate, then E = 1/V.

Currency Value

It is important to note that the value of a currency is always given in terms of another currency. Thus the value of a U.S. dollar in terms of British pounds is the £/$ exchange rate. The value of the Japanese yen in terms of dollar is the $/¥ exchange rate.

Currency Conversion Tables

Dollar	Rupee	Rupee	Dollar
$ 1	₨ 105	₨ 200	$ 1.9
$ 3	₨ 315	₨ 400	$ 3.81
$ 5	₨ 525	₨ 800	$ 7.62
$ 10	₨ 1051	₨ 4000	$ 38.08
$ 50	₨ 5253	₨ 8000	$ 76.15
$ 100	₨ 10505	₨ 40000	$ 381
$ 200	₨ 21010	₨ 80000	$ 762
$ 500	₨ 52525	₨ 160000	$ 1523
$ 1000	₨ 105050	₨ 400000	$ 3808
$ 3000	₨ 315150	₨ 800000	$ 7615
$ 5000	₨ 525250	₨ 1600000	$ 15231
₨105.05 per Dollar October, 2017		$0.0095 per Rupee October, 2017

Currency appreciation

Currency appreciation means that a currency appreciates with respect to another when its value rises in terms of the other. The dollar appreciates with respect to the yen if the ¥/$ exchange rate rises.

Currency depreciation

Currency depreciation on the other hand, means that a currency depreciates with respect to another when its value falls in terms of the other. The dollar depreciates with respect to the yen if the ¥/$ exchange rate falls.

Note that if the ¥/$ rate rises, then its reciprocal, the $/¥ rate, falls. Since the $/¥ rate represents the value of the yen in terms of dollars, this means that when the dollar appreciates with respect to the yen, the yen must depreciate with respect to the dollar.

The rate of appreciation (or depreciation) is the percentage change in the value of a currency over some period

Exchange rates between currencies have been highly unstable since the collapse of the Bretton Woods system of fixed exchange rates, which lasted from 1946 to 1973. Under the Bretton Woods system, exchange rates (e.g., the number of dollars it takes to buy a British pound or German mark) were fixed at levels determined by governments. Under the “floating” exchange rates we have had since 1973, exchange rates are determined by people buying and selling currencies in the foreign-exchange markets. The instability of floating rates has surprised and disappointed many economists and businessmen, who had not expected them to create so much uncertainty. The traditional exchange rate models seek for the identification of an equilibrium between two economies in order to calculate the fair value of the exchange rate. An equilibrium based on the relative valuation of an identical commodity, on relative inflation, on the relative level of real interest rates, etc.

There are two models for exchange rate.

Purchasing Power Parity (PPP)

The Purchasing Power Parity (PPP) model or else the “law of one price” estimates the adjustment needed on the exchange rate between countries in order for the exchange to be equivalent to each currency’s purchasing power.

PPP Basic Assumptions

PPP assumes that if there are no barriers to free trade the price of the same commodities must be the same everywhere in the world. Based on that assumption, the exchange rate between two economies must fluctuate towards a long-term value that ensures the equilibrium of commodity pricing.

Key Points regarding the PPP Analysis:

PPP analysis is based on several assumptions, including homogeneous products and absence of trade restrictions

PPP analysis can be used only for tradeable goods and not for non-tradeable goods such as services

In reality, only the prices of internationally traded goods tend to balance out

PPP analysis is useful for long-term currency valuation

There can significant divergences between currency valuations and PPP, especially in the short-term

PPP analysis is particularly useful for corporations, carry traders, and other long-term thinkers

PPP analysis is useless for short-term currency traders

Calculating the PPP

Basically, the price parity between two countries is formulated as:

■ e = Pd / Pf

This can be also expressed as:

■ Pd = e x Pf

where:

e = The PPP equilibrium exchange rate value

Pd = Domestic price level of a commodity

Pf = Foreign price level of a commodity

Actually, there are two different ways to calculate the Purchasing Power Parity (PPP):

(a) Absolute PPP {equilibrium = Domestic Price Index / Foreign Price Index}

(b) Relative PPP {equilibrium = Domestic Price Index – Foreign Price Index}

Practically, in order to calculate PPP:

(i) Create a basket of traded goods and services in two countries

(ii) Price the two baskets

(iii) Compare the two baskets and identify an exchange rate that would make the pricing of those two baskets identical

The Problems when using the PPP

PPP assumes that there are no barriers to trade. But actually, there are a lot of barriers and other similar anomalies in international trade. Moreover, Forex currencies can remain overvalued for long periods of time. For example, the Swiss Franc (CHF) remains overvalued, on a PPP basis, since the 80s.

These are some factors disturbing PPP analysis:

• The traded goods are not perfectly homogeneous
• There are several barriers to international trade
• Restricting government policies (such as tariffs)
• Speculation in the prices of goods
• Transportation costs
• Transaction costs
• Multinational corporations usually delay the re-adjustment of prices across different markets
• PPP analysis may not hold for services and other non-tradable goods
• PPP analysis may not hold even for homogeneous commodities as the pricing between economies depends on local market dynamics

2: The Portfolio Balance Approach

The Portfolio Balance approach is a modern theory based on the relationship between the relative price of bonds and exchange rates.

The Portfolio Balance Approach Explained

The portfolio balance approach is an extension of the monetary exchange rate models focusing on the impact of bonds. According to this approach, any change in the economic conditions of a country will have a direct impact on the demand and supply for the domestic and the foreign bond. This shift in the demand/supply for bonds will in turn influence the exchange rate between the domestic and foreign economies.

The key advantage of the portfolio approach when compared to traditional approaches is that the financial assets tend to adjust considerably faster to news economic conditions than tradeable goods. Nevertheless, based on empirical evidence, the portfolio balance approach is not an accurate predictor of exchange rates.

The Assumptions of Portfolio Balance Approach

The portfolio balance approach is based on several assumptions:

1. The purchasing power parity (PPP) does not hold
2. The uncovered interest parity does not hold
3. The exchange rate is expected unchanged
4. Only three (3) assets are available for investment for each household: money, domestic bonds, and foreign bonds
5. Bonds are not perfect substitutes
6. Assumes perfect capital mobility without capital controls and similar barriers to investment
7. Assumes narrow transaction costs and high completion in the money markets

Portfolio Balance Approach Key Points

Emphasizes on the importance of global financial markets (especially as concerns the bond markets)

Assumes the existence of arbitrage between two economies

Offers a realistic and simplistic analysis framework

The portfolio balance approach, based on empirical evidence, hasn’t proven an accurate predictor of exchange rates

Gosh et al., 2002 used their own exchange rate regime classification, discovered a slight superiority of fixed exchange rate regimes to stimulate economic growth, but the results of the study are not robust. The authors reached the conclusion that there is not a strong correlation between the adopted exchange rate regime and the economic growth

Mahmood et al(2011). Role of exchange rate in affected the macroeconomic performance of any country is of leading nature. The study has been conducted to investigated whether uncertainty or fluctuations in exchange rate affect the macroeconomic variables in Pakistan.They used a data over the period of 1995 to 2005. . GARCH model has been applied in this study to calculate volatility of real exchange rate and Ordinary Least Square regression technique has been used to investigate the relationship between dependent and independent variables. It is also concluded that exchange rate volatility positively affects GDP, Growth rate and trade openness and negatively affects the FDI.

Munyama and Todani (2005) have reported a positive relationship between exchange rate volatility and export performance, Kasman and Kasman (2005) found positive effects of exchange rate volatility on trade whereas, studies by Esquivel and Felipe (2002) and Doganlar (2002) have found a negative relationship between exchange rate volatility and exports.found that incorporating exchange rate regime shifts leads to reduction in the estimated volatility persistence. Engle F. R. and Rangel (2008) applied the spline–GARCH model in modeling volatility structural breaks and long memory models. Baillie and Morana (2009) used the adaptive FIGARCH model

Methodology

This study estimates a cross-country growth regression on a panel-data set over the 1973–2015  period to investigate whether the nature of the exchange rate regime has an effect on economic growth. This section describes the econometric specification used and then discusses the expected signs on the coefficients of the explanatory variables.

The following equation used:

GDPPC= f (EXP, WPI, INF)

Where

GDPPC=Gross Domestic Product Per Capita (current US$)

INF=Inflation, consumer prices (annual %)

WPI=Whole sale price index (2010=100)

EXP=Export of goods and services(% of GDP)

The objective of this study is to determine the volatility of exchange rate in Pakistan as well as to establish the effect of this volatility on the economic growth of Pakistan. The study VAR and ADF Models developeddetermining the volatility of exchange rate for the period under study..To study the impact of exchange rate on the GDP growth of Pakistan, we take variables; GDP growth as dependent variable and ER (exchange rate), wholesale price index, inflation and export as independent variable. Source used for the data is WDI (World Development Indicator).

The impact exchange rate on economic growth is discussed in various studies most of them used

Granger causality test, Johansen co-integration approach, Vector Error Correction Mechanism and ordinary least square method [see for example.Akpan and Atan(2016). Aslam (2016).Jakob (2016).Habib et al (2016). M. AzeemNaseer (2013)Kasman and Kasman (2005) . Engle F. R. and Rangel (2008).Stancik (2007)Schnabl (2007).

Model:

The general Equation of the model is as follow:

GDPPC = β0+ β₁ EXP+β₂ WPI+ β₃ INF+ ɛ.

Where:

GDP Per capita is a dependent variable and three independent variables export, wholesale price index and inflation.ɛ is the error tern in the equation.

Variables Explanation

GDP:

Per capita GDP is a measure of the total output of a country that takes gross domestic product (GDP) and divides it by the number of people in the country. The per capita GDP is especially useful when comparing one country to another, because it shows the relative performance of the countries. A rise in per capita GDP signals growth in the economy and tends Toreflectanincrease in productivity.

Export

 Exports are the goods and services produced in one country and purchased by citizens of another country. It doesn’t matter what the good or service is. It doesn’t matter how it is sent. It can be shipped, sent by email, or carried in personal luggage on a plane. If it is produced domestically and sold to someone from a foreign country, it is an export.

WPI

The Wholesale Price Index or WPI is “the price of a representative basket of wholesale goods”. Some countries use the changes in this index to measure inflation in their economies. The wholesale price index (WPI) is based on the wholesale price of a few relevant commodities of over 240 commodities available. The commodities chosen for the calculation are based on their importance in the region and the point of time the WPI is employed.

Inflation

Inflation is defined as a sustained increase in the general level of prices for goods and services in a county, and is measured as an annual percentage change. Under conditions of inflation, the prices of things rise over time. Put differently, as inflation rises, every dollar you own buys a smaller percentage of a good or service. When prices rise, and alternatively when the value of money falls you have inflation.

CHAPTER 4

ESTIMATION AND DISCUSSION OF RESULTS

For finding the impact of exchange rate on economic growth of Pakistan, first of all this study estimated the unit roots. Then VAR, ECM and Johanson co-integration technique is applied.

Unit root test

A unitroot test confirms whether a time series variable is non-stationary and possesses a unit root or not. The null hypothesis is generally defined as the presence of a unit root and the alternative hypothesis is either stationarity, trend stationarity or explosive root depending on the test used.

In general, the approach to unit root testing implicitly assumes that the time series to be tested [y _t]^T_t=1{\displaystyle [y_{t}]_{t=1}^{T}} can be written as

Y _t =D _{t   +}z _t +E _t

{\displaystyle y_{t}=D_{t}+z_{t}+\varepsilon _{t}}Where,

• {\displaystyle D_{t}}D _t is the deterministic component (trend, seasonal component, etc.)
• {\displaystyle z_{t}}z_tis the stochastic component.
• {\displaystyle \varepsilon _{t}}E _tis the stationary error process.

The task of the test is to determine whether the stochastic component contains a unit root or is stationary

Augmented Dickey–Fuller test

In statistics and econometrics, an augmented Dickey–Fuller test (ADF) tests the null hypothesis that a unit root is present in a time series sample. The alternative hypothesis is different depending on which version of the test is used, but is usually stationarity or trend-stationarity. It is an augmented version of the Dickey–Fuller test for a larger and more complicated set of time series models. The augmented Dickey–Fuller (ADF) statistic, used in the test, is a negative number. The more negative it is, the stronger the rejection of the hypothesis that there is a unit roots at some level of confidence.

Table 1.The Results of Augmented Dicky-Fuller (ADF) Test for Unit Root:

ADF Test Statistics

Variables	At level					At 1st difference
	t-observation			p-value		t-observation		p-value
	Without trend		With trend	Without trend	With trend	Without trend	With trend	Without trend	With trend
GDPPC	2.458	0.409		1.000	0.998	-4.659	-5.338	0.0005	0.0004
EXPORT	-1.467		-1.285	0540	0.878	-5.914	-5.891	0.0000	0.0001
WPI	3.5951		1.5493	1.0000	1.000	-3.9118	-3.8429	0.0047	0.0255
INF	-3.2751		-3.204	0.0225	0.098	-7.124	-7.1553	0.0000	0.0000

Phillips Perron

The Phillips–Perron test (named after Peter C. B. Phillips and PierrePerron) is a unit root test. That is, it is used in time series analysis to test the null hypothesis that a time series is integrated of order 1.

Table 2.The Results of Phillips Perron Test for Unit Root:

Phillips perron test statistics

Variables	At level				At 1st difference
	t-observation		p-value		t-observation		p-value
	Without trend	With trend	Without trend	With trend	Without trend	With trend	Without trend	With trend
GDPPC	2.444	0.285	1.000	0.997	-4.670	-5.266	0.0005	0.0005
EXPORT	-1.544	-1.381	0.5016	0.852	-5.918	-5.877	0.000	0.0001
WPI	2.9658	-9.00E-05	1.0000	0.9950	-2.8222	-3.3981	0.0639	0.0656
INF	-3.2284	-3.1534	0.0252	0.1078	-7.1116	-7.1443	-0.0000	0.0000

Augmented Dicky-Fuller and Philip Perron results show that some variables have integrated order I (0), and some have I (1). After testing the stationary of variables next step is to check the long run relationship among the variables.

Vector Auto regression Results

Vector autoregression (VAR) is a stochastic process model used to capture the linear interdependencies among multiple time series. VAR models generalize the univariate autoregressive model (AR model) by allowing for more than one evolving variable. All variables in a VAR enter the model in the same way: each variable has an equation explaining its evolution based on its own lagged values, the lagged values of the other model variables, and an error term. VAR modeling does not require as much knowledge about the forces influencing a variable as do structural models with simultaneous equations: The only prior knowledge required is a list of variables which can be hypothesized to affect each other intertemporally.

Vector Autoregression Estimates
Date: 10/28/17   Time: 09:07
Sample (adjusted): 1975 2015
Included observations: 41 after adjustments
Standard errors in ( ) & t-statistics in [ ]
	GDPPC	INF	WPI	EXPORT
GDPPC(-1)	1.164351	0.027855	0.004126	-0.008708
	(0.20803)	(0.01320)	(0.01381)	(0.00510)
	[ 5.59693]	[ 2.10997]	[ 0.29868]	[-1.70876]
GDPPC(-2)	-0.250293	-0.004418	0.027143	0.011970
	(0.20363)	(0.01292)	(0.01352)	(0.00499)
	[-1.22916]	[-0.34194]	[ 2.00735]	[ 2.39973]
INF(-1)	0.246520	0.480699	-0.203802	-0.040598
	(2.44093)	(0.15490)	(0.16209)	(0.05979)
	[ 0.10099]	[ 3.10336]	[-1.25736]	[-0.67899]
INF(-2)	-0.185758	0.149896	0.372755	-0.047398
	(2.10026)	(0.13328)	(0.13947)	(0.05145)
	[-0.08845]	[ 1.12469]	[ 2.67274]	[-0.92131]
WPI(-1)	0.396773	-0.322734	1.289847	0.060296
	(3.54470)	(0.22494)	(0.23538)	(0.08683)
	[ 0.11193]	[-1.43476]	[ 5.47979]	[ 0.69443]
WPI(-2)	0.801123	0.138474	-0.519846	-0.096623
	(3.52260)	(0.22354)	(0.23392)	(0.08629)
	[ 0.22742]	[ 0.61947]	[-2.22237]	[-1.11978]
EXPORT(-1)	10.41240	1.166985	0.416642	0.766814
	(7.05360)	(0.44761)	(0.46839)	(0.17278)
	[ 1.47618]	[ 2.60716]	[ 0.88952]	[ 4.43812]
EXPORT(-2)	-9.745523	-1.107482	-0.314301	0.113529
	(7.21904)	(0.45811)	(0.47937)	(0.17683)
	[-1.34998]	[-2.41752]	[-0.65565]	[ 0.64201]
C	19.47231	-3.583568	-9.247716	1.988762
	(55.9932)	(3.55322)	(3.71818)	(1.37156)
	[ 0.34776]	[-1.00854]	[-2.48716]	[ 1.45000]
R-squared	0.986026	0.597623	0.995637	0.814224
Adj. R-squared	0.982532	0.497029	0.994546	0.767779
Sum sq. resids	68314.94	275.0996	301.2349	40.98991
S.E. equation	46.20435	2.932041	3.068158	1.131784
F-statistic	282.2457	5.940933	912.7688	17.53125
Log likelihood	-210.2519	-97.19948	-99.06001	-58.17144
Akaike AIC	10.69521	5.180462	5.271220	3.276655
Schwarz SC	11.07136	5.556612	5.647370	3.652805
Mean dependent	584.0079	8.658526	41.44812	13.53874
S.D. dependent	349.5967	4.134267	41.54536	2.348622
Determinant resid covariance (dof adj.)		112837.9
Determinant resid covariance		41871.59
Log likelihood		-450.8744
Akaike information criterion		23.74997
Schwarz criterion		25.25457

Johanson Co-integration:

The Johansen test can be seen as a multivariate generalization of the augmented Dickey Fuller test. The generalization is the examination of linear combinations of variables for unit roots. The Johansen test and estimation strategy – maximum likelihood – makes it possible to estimate all cointegrating vectors when there are more than two variables.1 If there are three variables each with unit roots, there are at most two cointegrating vectors. More generally, if there are n variables which all have unit roots, there are at most n − 1 cointegrating vectors. The Johansen test provides estimates of all cointegrating vectors. The results of the co-integration analysis containing the Trace statistics and Eigen values along with their probabilities are reported below.

Johanson Co-integration Test Results:

The Johansen-Juselius Cointegration test result indicates two co-integrating equation at the 0.05 level of significance of the variables such asGDPPC, Export,Wholesale price index and Inflation respectively. Thus, the null hypothesis of no co-integration is hereby rejected at the 0.05 per cent level of significance. This test therefore provides an evidence of a long run relationship between the variables of the study. Below table presents the test results of Johansen-Juselius Cointegration test result.

Date: 10/28/17   Time: 09:04
Sample (adjusted): 1976 2015
Included observations: 40 after adjustments
Trend assumption: Linear deterministic trend
Series: GDPPC INF WPI EXPORT
Lags interval (in first differences): 1 to 2
Unrestricted Cointegration Rank Test (Trace)
Hypothesized		Trace	0.05
No. of CE(s)	Eigenvalue	Statistic	Critical Value	Prob.**
None *	0.582833	69.36965	47.85613	0.0002
At most 1 *	0.421272	34.39893	29.79707	0.0138
At most 2	0.251830	12.52205	15.49471	0.1335
At most 3	0.022665	0.917041	3.841466	0.3383
Trace test indicates 2 cointegratingeqn(s) at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
Unrestricted Cointegration Rank Test (Maximum Eigenvalue)
Hypothesized		Max-Eigen	0.05
No. of CE(s)	Eigenvalue	Statistic	Critical Value	Prob.**
None *	0.582833	34.97073	27.58434	0.0047
At most 1 *	0.421272	21.87688	21.13162	0.0392
At most 2	0.251830	11.60500	14.26460	0.1264
At most 3	0.022665	0.917041	3.841466	0.3383
Max-eigenvalue test indicates 2 cointegratingeqn(s) at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
Unrestricted Cointegrating Coefficients (normalized by b’*S11*b=I):
GDPPC	INF	WPI	EXPORT
-0.014224	0.197981	0.125196	-0.130704
-0.004973	-0.090258	0.085890	-0.009373
-0.011017	-0.296100	0.046605	-0.251989
0.013569	0.125985	-0.068675	-0.406537
Unrestricted Adjustment Coefficients (alpha):
D(GDPPC)	3.259235	18.01712	-8.217986	4.166274
D(INF)	-2.217191	0.519459	0.223879	0.092386
D(WPI)	-1.247416	-0.573371	-0.532471	0.317186
D(EXPORT)	-0.067674	-0.225676	0.412241	0.076167
1 Cointegrating Equation(s):		Log likelihood	-438.0921
Normalized cointegrating coefficients (standard error in parentheses)
GDPPC	INF	WPI	EXPORT
1.000000	-13.91882	-8.801773	9.188990
	(4.03629)	(0.75851)	(5.37107)
Adjustment coefficients (standard error in parentheses)
D(GDPPC)	-0.046359
	(0.11055)
D(INF)	0.031537
	(0.00565)
D(WPI)	0.017743
	(0.00709)
D(EXPORT)	0.000963
	(0.00267)
2 Cointegrating Equation(s):		Log likelihood	-427.1536
Normalized cointegrating coefficients (standard error in parentheses)
GDPPC	INF	WPI	EXPORT
1.000000	0.000000	-12.47808	6.018793
		(1.20521)	(9.72426)
0.000000	1.000000	-0.264125	-0.227763
		(0.08483)	(0.68444)
Adjustment coefficients (standard error in parentheses)
D(GDPPC)	-0.135955	-0.980924
	(0.10611)	(1.53216)
D(INF)	0.028954	-0.485847
	(0.00581)	(0.08394)
D(WPI)	0.020594	-0.195213
	(0.00735)	(0.10608)
D(EXPORT)	0.002085	0.006971
	(0.00276)	(0.03981)
3 Cointegrating Equation(s):		Log likelihood	-421.3511
Normalized cointegrating coefficients (standard error in parentheses)
GDPPC	INF	WPI	EXPORT
1.000000	0.000000	0.000000	24.69952
			(17.2230)
0.000000	1.000000	0.000000	0.167653
			(0.33503)
0.000000	0.000000	1.000000	1.497083
			(1.93924)
Adjustment coefficients (standard error in parentheses)
D(GDPPC)	-0.045416	1.452420	1.572546
	(0.12842)	(2.52804)	(1.09267)
D(INF)	0.026488	-0.552138	-0.222533
	(0.00716)	(0.14096)	(0.06092)
D(WPI)	0.026461	-0.037549	-0.230234
	(0.00892)	(0.17554)	(0.07587)
D(EXPORT)	-0.002457	-0.115094	-0.008644
	(0.00311)	(0.06127)	(0.02648)

The main focus of the study is to assess how real GDP in the long run reacts to volatility in exchange rate. A cointegration test in line with Johansen’s maximum likelihood of co-integration test is conducted in order to determine the number of long run equilibrium relationship or cointegrating vectors among the variables. The results show that both the trace statistics and the maximum Eigen value, suggest the presence of two cointegrating equation among the fourvariables in the Pakistan economy at 5 percent level in line with Osterwald-Lenum critical values. This shows that there is a long-run relationship between real GDP and the variables used in the model.

ECM  Test results

Error Correction Mechanism An over parameterized error correction as shown below was estimated to determine the significant and non significant variables.  At this level, the over parameterized model is difficult to interpret in any meaningful way; its main function is to allow us to identify the main dynamic patterns in the model.

Vector Error Correction Estimates
Date: 10/28/17   Time: 09:08
Sample (adjusted): 1976 2015
Included observations: 40 after adjustments
Standard errors in ( ) & t-statistics in [ ]
CointegratingEq:	CointEq1
GDPPC(-1)	1.000000
INF(-1)	-13.91882
	(4.03629)
	[-3.44842]
WPI(-1)	-8.801773
	(0.75851)
	[-11.6041]
EXPORT(-1)	9.188990
	(5.37107)
	[ 1.71083]
C	-222.1597
Error Correction:	D(GDPPC)	D(INF)	D(WPI)	D(EXPORT)
CointEq1	-0.046359	0.031537	0.017743	0.000963
	(0.11055)	(0.00565)	(0.00709)	(0.00267)
	[-0.41935]	[ 5.58104]	[ 2.50171]	[ 0.36088]
D(GDPPC(-1))	0.184561	-0.001677	-0.031525	-0.008865
	(0.23261)	(0.01189)	(0.01492)	(0.00561)
	[ 0.79343]	[-0.14104]	[-2.11245]	[-1.57950]
D(GDPPC(-2))	0.123347	0.022533	0.028111	-0.003194
	(0.24673)	(0.01261)	(0.01583)	(0.00595)
	[ 0.49993]	[ 1.78674]	[ 1.77596]	[-0.53647]
D(INF(-1))	-1.937053	-0.300056	-0.286410	0.087930
	(2.63742)	(0.13481)	(0.16921)	(0.06364)
	[-0.73445]	[-2.22574]	[-1.69268]	[ 1.38178]
D(INF(-2))	-1.249266	-0.309358	0.091973	0.036332
	(2.20737)	(0.11283)	(0.14162)	(0.05326)
	[-0.56595]	[-2.74181]	[ 0.64946]	[ 0.68218]
D(WPI(-1))	4.003790	0.035571	0.945154	-0.045944
	(3.44864)	(0.17628)	(0.22125)	(0.08321)
	[ 1.16098]	[ 0.20179]	[ 4.27189]	[-0.55216]
D(WPI(-2))	-2.185768	0.024329	-0.025343	0.051028
	(3.44479)	(0.17608)	(0.22100)	(0.08312)
	[-0.63451]	[ 0.13817]	[-0.11467]	[ 0.61395]
D(EXPORT(-1))	10.37023	1.207217	0.034606	-0.113407
	(7.66140)	(0.39161)	(0.49152)	(0.18485)
	[ 1.35357]	[ 3.08268]	[ 0.07040]	[-0.61350]
D(EXPORT(-2))	4.185375	0.070811	0.178336	-0.151963
	(8.35503)	(0.42707)	(0.53602)	(0.20159)
	[ 0.50094]	[ 0.16581]	[ 0.33270]	[-0.75383]
C	14.76561	-1.497036	0.170434	0.390196
	(11.5995)	(0.59291)	(0.74417)	(0.27987)
	[ 1.27295]	[-2.52490]	[ 0.22902]	[ 1.39420]
R-squared	0.198683	0.687249	0.653348	0.188320
Adj. R-squared	-0.041712	0.593424	0.549353	-0.055183
Sum sq. resids	72487.25	189.3902	298.3529	42.19862
S.E. equation	49.15528	2.512570	3.153585	1.186010
F-statistic	0.826486	7.324770	6.282470	0.773377
Log likelihood	-206.8033	-87.85614	-96.94549	-57.82770
Akaike AIC	10.84016	4.892807	5.347275	3.391385
Schwarz SC	11.26238	5.315027	5.769495	3.813605
Mean dependent	31.53634	-0.459125	3.385800	-0.006713
S.D. dependent	48.16110	3.940461	4.697711	1.154581
Determinant resid covariance (dof adj.)		121033.6
Determinant resid covariance		38295.80
Log likelihood		-438.0921
Akaike information criterion		24.10460
Schwarz criterion		25.96237

, the results indicate that exchange rate (EXRATE) has a statistically significant positive influence on economic growth in Pakistan in the long-run. This finding in the long run is consistent with the study findings of Rapetti, Skott and Razmi (2011), and Rodrik (2008). But in the short-run, exchange rate (EXRATE) has a statistically significant negative influence on economic growth as shown in above table. The short run effect aligns with the findings of Basirat, Nasirpour and Jorjorzadeh (2014).

CHAPTER 5

CONCLUSION

This study attempts to offer evidence on the relationship among gross domestic product per capita (gdppc), wholesale price index (WPI) ,export (expt), inflation (INF) in Pakistan. The series used in the analysis was tested for stationarity, using Augmented Dickey-Fuller (ADF) and Phillip-Perron (PP). The result indicted that the variables are not stationary at level, though stationary at first difference. On the Johansen Cointegration test, it shows the presence of long-run relationship among the cointegrating variables. Furthermore, an Engle-Granger 2-Step procedure was applied and an error correction model (ECM) was developed from long-run static model. The error correction term in the short-run dynamic model has a statistically significant coefficient with the appropriate negative sign and this is a requirement for dynamic stability of the model.

The impact of exchange rate fluctuation on Pakistani economic growth is investigated by incorporating the calculated volatility of exchange rates.

The objective of the study is to measure the impact of exchange rate fluctuation on economic growth in Pakistan both in the long and short run. It was established that there is a link between exchange rate fluctuation and economic growth in Pakistan in both the long and short run. The estimation results showed that volatility in exchange rate had no influence on economic growth while its actual exchange rate had positive effect on economic growth in Pakistan in the long run.

This study established that there is a positive but insignificant relationship between economic growth and exchange rate fluctuation in the short run. Empirically, the insignificant positive relationship between exchange rate fluctuation and economic growth was attributed to the influence of the monetary authorities in mitigating exchange rate fluctuation in Pakistan. Observably, the high volatility persistence and its significant impact on the Pakistani economy of oil prices could have been due to OPEC’s regulations, global recession or change in the structure of the Pakistani economy since Pakistan’s foreign exchange earnings are more than 90 per cent dependent on receipts from crude oil Export.

Policy Implications

This study’s findings from the policy perspective are helpful to policy makers, government and monetary authorities since the exchange rate as an economic indicator is significant to achieving economic growth and development. Based on the findings of this study, it was therefore recommended that:

1. There should be an increase in the exchange rate of Pakistan in order to enhance economic growth.
2. The government should Endeavour to stabilize the exchange rate of Pakistan in order to achieve economic growth because of the current high fluctuation of exchange value.
3. Investors should consider fluctuations in other macroeconomic variables rather than fluctuation in the exchange rate market to guide their decisions in order to ascertain where to direct investments for profit maximization.
4. The Pakistani economy need be diversified to enhance economic growth.
5. The government should encourage domestic production and consumption of goods and services in order to curtail the effects of exchange rate fluctuation on other macroeconomic variables.
6. Oil price should be stabilized since it was the most volatile variable during the years of study in order to ensure economic growth in Pakistan.
7. Lastly, to maintain and sustain exchange rate and economic stability, more attention need be paid by the government to other more volatile macroeconomic variables like oil price and inflation rate in Pakistan.

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