# An analysis of impact of exchange rate on inflation rate in Indian economy.

Introduction

The effects of the uncertainties caused by the volatility in exchange rates and inflation on macroeconomic variables have been subject to extensive theoretical and empirical research. Empirical findings indicate significant impact of exchange rate uncertainty on macroeconomic variables such as output, trade and investment. Similarly, inflation uncertainty appears to affect variables such as output, employment and interest rates. The relationship between exchange rate and inflation uncertainties, on the other hand, has been largely overlooked in the literature. This paper attempts to investigate the impact of exchange rate on inflation rate in India.

India in the past has mostly been a closed economy, following protectionist policies of development. Foreign exchange management and control has been an important tool of lending protection to the domestic economy, checking capital flight, maintaining a comfortable reserve of valuable foreign exchange for development needs and import of essential goods and encouraging exports. To attain these objectives India followed a 'fixed exchange rate' system till the economic crisis of 1991.

The Indian forex market had been heavily controlled since the 1950s, along with increasing trade controls designed to foster import substitution. Consequently, both the current and capital accounts were closed and foreign exchange was made available by the RBI through a complex licensing system. India's post-independence development strategy was both inward-looking and highly interventionist, consisting of import protection, complex industrial licensing requirements, financial repression, and substantial public ownership of heavy industry. However, macroeconomic policy sought stability through low monetary growth and moderate public sector deficits. The current account was in surplus for most years until 1950, and there was a reasonable cushion of official reserves.

Since 1950, India ran continued trade deficits that increased in magnitude in the 1960s. Furthermore, the government of India had a budget deficit problem and could not borrow money from abroad or from the private corporate sector; due to that sector's negative savings rate. As a result, the government issued bonds to the RBI, which increased the money supply.

In 1966, foreign aid was finally cut off and India was told it had to liberalise its restrictions on trade before foreign aid would again materialize. The response was devaluation accompanied by liberalization.

The foreign Exchange Regulation Act (1973) was consolidated and amended in 1947 to regulating certain payments, dealings in foreign exchange and securities, transactions indirectly affecting foreign exchange and the import and export of currency, for the conservation of the foreign exchange resources of the country and the proper utilization thereof in the interests of the economic development of the country.

During the first half of the 1980s, the current account deficit stayed below one and a half percent of GDP. While export growth was slow, the trade deficit was kept in check, as a rapid rise in domestic petroleum production permitted savings on energy imports. At the same time, the high proportion of concessioner external financing kept debt service down.

In the second half of the 1980s, current account deficits widened. India's development policy emphasis shifted from import substitution towards export-led growth, supported by measures to promote exports and liberalize imports for exporters. The government began a process of gradual liberalization of trade, investment, and financial markets. Import and industrial licensing requirements were eased and tariffs replaced some quantitative restrictions. Export growth was rapid, due to the initial measures of deregulation and improved competitiveness associated with the real depreciation of the rupee.

By 1990-91, India was increasingly vulnerable to shocks as a result of its rising current account deficits and greater reliance on commercial external financing. Exports markets were weak in this period leading up to India's crisis, as world growth declined steadily from 4/4 percent in 1988 to 2% percent in 1991. The decline was even greater for U.S. growth, India's single largest export destination. U.S. growth fell from 3.9 percent in 1988 to 0.8 percent in 1990 and to -1 percent in 1991. Consequently, India's export volume growth slowed to 4 percent in 1990-91.

India's balance of payments in 1990-91 also suffered from capital account problems due to a loss of investor confidence. The widening current account imbalances and reserve losses contributed to low investor confidence, which was further weakened by political uncertainties and finally by a downgrade of India's credit rating by the credit rating agencies. The government's economic policies changed drastically in that year, the 1991 liberalization was an extension of earlier, albeit slower, reform efforts that had begun in the 1970s when India relaxed restrictions on imported capital goods as part of its industrialization plan. The Import-Export Policy of 1985-1988 replaced import quotas with tariffs. After 1991, the Government of India further reduced trade barriers by lowering tariffs on imports. In the postliberalization era, quantitative restrictions have not been significant. At that time Indian economy was opened up and foreign exchange regulations were significantly relaxed, the exchange rate fluctuated according to market demand and supply caused by international trade and capital flows to and from India and other speculative forces.

In July 1991 the Indian government devalued the rupee by between 18 and 19 percent. The government also changed its trade policy from its highly restrictive form to a system of freely tradable EXIM scrips which allowed exporters to import 30 percent of the value of their exports (Gupta, pp.73-74)

The task facing India in the early 1990s was, therefore, to gradually move from total control to a functioning forex market. The move towards a market--based exchange rate regime in 1993 and the subsequent adoption of current account convertibility were the key measures in reforming the Indian foreign exchange market.

Reforms in the foreign exchange market focused on market development with prudential safeguards without destabilizing the market. Authorized Dealers of foreign exchange have been allowed to carry on a large range of activities. Banks have been given large autonomy to undertake foreign exchange market, a large number of products have been introduced and entry of newer players has been allowed in the market.

The Indian approach to opening the external sector and developing the foreign exchange market in a phased manner from current account convertibility to the ongoing process of capital account opening is perhaps the most striking success relative to other emerging market economies.

Chronology of India's exchange rate policies

* 1947 (When India became member of IMF): Rupee tied to pound

* 18 September, 1949: Pound devalued; India maintained par with pound

* 6 June, 1966: Rupee is devalued

* 18 November, 1967: UK devalued pound, India did not devalue

* August 1971: Rupee pegged to gold/dollar, international financial crisis

* 18 December, 1971: Dollar is devalued

* 20 December, 1971: Rupee is pegged to pound sterling again

* 1971-1979: The Rupee is overvalued due to India's policy of import substitution

* 23 June, 1972: UK floats pound, India maintains fixed exchange rate with pound

* 1975: India links rupee with basket of currencies of major trading partners. Although the basket is periodically altered, the link is maintained until the 1991 devaluation.

* July 1991: Rupee devalued by 18-19 %

* March 1992: Dual exchange rate, LERMS, Liberalised Exchange Rate Management System

* March 1993: Unified exchange rate: $1 = Rs 31.37

* 1993/1994: Rupee is made freely convertible for trading, but not for investment purposes

In the present study, an attempt has been made to evaluate the impact of exchange rate on India's inflation rate. Section I presents the link behind exchange rate and inflation rate. Section II gives the survey of literature related to foreign exchange rate and inflation rate. In Section III of the paper data source and methodology of the study has been dealt with. Section IV attempts to statistically evaluate the impact of exchange rate on India's inflation rate. In the last section V, summary and conclusions of the study has been given.

The effects of the uncertainties caused by the volatility in exchange rates and inflation on macroeconomic variables have been subject to extensive theoretical and empirical research.

Exchange rate system has an important role in reducing or minimizing the risk of fluctuations in exchange rates, which will have an impact on the economy. In the system of floating exchange rates, exchange rate fluctuations can have a strong impact on the level of prices through the aggregate demand (AD) and aggregate supply (AS). On the aggregate supply, depreciation (devaluation) of domestic currency can affect the price level directly through imported goods that domestic consumers pay. However, this condition occurs if the country is the recipient countries of international prices (international price taker). Non direct influence from the depreciation (devaluation) of currency against the price level of a country can be seen from the price of capital goods (intermediate goods) imported by the manufacturer as an input. The weakening of exchange rate will cause the price of inputs more expensive, thus contributing to a higher cost of production. Manufacturers will certainly increase the cost to the price of goods that will be paid by consumers. As a result, the price level aggregate in the country increases or if it continues it will cause inflation.

Exchange rate movements can influence domestic prices via effect on aggregate supply and demand. On the supply side, exchange rates could affect prices paid by the domestic buyers of imported goods directly. In an open economy (an international price taker), when the currency depreciates it will result in higher import prices and vice-versa. Exchange rate fluctuations could have an indirect supply effect on domestic prices. The potentially higher cost of imported inputs associated with an exchange rate deprecation increases marginal cost and leads to higher prices of domestically produced goods (Hyder and Shah, 2004). Further import-competing firms might increase prices in response to an increase in foreign competitor in order to improve profit margins. The extent of such price adjustment depends on a variety of factors such as market structure, nature of government exchange rate policy, or product substitutability.

Exchange rate fluctuations can also affect aggregate demand. To a certain extent, exchange rate depreciations (appreciations) increase (decrease) foreign demand for domestic goods and services, causing increase (decrease) in net exports and hence aggregate demand (Hyder and Shah, 2004). This may increase real output. Furthermore, the expansion in domestic demand and gross national product may bid up input prices and accelerate wage demands by workers seeking higher wages to maintain real wages. The nominal wage rise may result in further price increases.

Review of Literature

The above studies show mixed and inconclusive results on the relationship between exchange rate and inflation. Most of the studies reveal that there is positive relation between exchange rate depreciation and inflation rate. It has also been seen that the research on impact of exchange rate and inflation is largely overlooked in the Indian Economy.

Data Source and Methodology:

Data Source:

To achieve the stated objective of the paper, secondary data has been used. Major data source is Economic Survey of India, especially on Database on Indian economy. The time period of the study is 1991 Q1 to 2009 Q2. For theoretical explanation standard work on the subject including internet has been consulted.

For finding empirical results eviews has been used.

Methodology:

The first step is to test for stationarity of the series with the help of unit root tests. In the presence of non-stationary variables, there is possibility of spurious regression. The second step is to test for cointegration if the variables are non-stationary in their levels and stationary in first difference. Once the cointegration has been established amongst the variables, the third step is to formulate Error-Correction Model (ECM) to examine the casual relationship between exchange rate and inflation rate.

For correcting the problem of autoregression and heteroscedasticity we have used GARCH model for finding long-run impact. Generalised Autoregressive Conditional Heteroskedasticity (GARCH) models are specifically designed to model and forecast conditional variances. The variance of the dependent variable is modeled as a function of past values of the dependent variable and independent, or exogenous variables.

The GARCH (1,1) Model:

GARCH (1,1) model specifications:

Yt = X't [sup.[theta]] + [[member of].sub.t] (1)

[[sigma].sup.2.sub.t] = [omega] + [alpha] [[member of].sup.2.sub.t-1] + [beta] [[sigma].sup.2.sub.t-1] (2)

in which the mean equation given in equation 1 is written as a function of exogenous variables with an error term. Since is the one-period ahead forecast variance based on past information, it is called conditional variance. The conditional variance equation is specified in equation 2 is a function of three terms:

The constant term [omega]

News about volatility from the previous period, measured as the lag of the squared residual from the mean equation [[member of].sup.2.sub.t-1] (the ARCH term). Last period's forecast variance [[sigma].sup.2.sub.t-1] (the GARCH term).

The (1, 1) in GARCH (1, 1) refers to the presence of a first-order autoregressive GARCH term (the first term in parentheses) and a first-order moving average ARCH term (the second term in parentheses). An ordinary ARCH model is a special case of a GARCH specification in which there are no lagged forecast variances in the conditional variance equation--i.e., a GARCH (0, 1).

This specification is often interpreted in a financial context, where an agent or trader predicts this period's variance by forming a weighted average of a long term average (the constant), the forecasted variance from last period (the GARCH term), and information about volatility observed in the previous period (the ARCH term). If the asset return was unexpectedly large in either the upward or the downward direction, then the trader will increase the estimate of the variance for the next period. This model is also consistent with the volatility clustering often seen in financial returns data, where large changes in returns are likely to be followed by further large changes. There are two equivalent representations of the variance equation that may aid in interpreting the model:

* If we recursively substitute for the lagged variance on the right-hand side of equation 2 we can express the conditional variance as a weighted average of all of the lagged squared residuals:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (3)

[[sigma].sup.2.sub.t]

We see that the GARCH(1,1) variance specification is analogous to the sample variance, but that it down-weights more distant lagged squared errors.

* The error in the squared returns is given by [V.sub.t] [[epsilon].sup.2] - [[sigma].sup.2.sub.t]. Substituting for the variances in the variance equation and rearranging terms we can write our model in terms of the errors:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (4)

Thus, the squared errors follow a heteroskedastic ARMA(1,1) process. The autoregressive root which governs the persistence of volatility shocks is the sum of a plus p in many applied settings, this root is very close to unity so that shocks die out rather slowly.

Testing for causality

One of the good features of VAR models is that they allow us to test for the direction of causality. Causality in econometrics is somewhat different to the concept in everyday use; it refers more to the ability of one variable to predict (and therefore cause) the other. Suppose two variables, say [Y.sub.t] and [X.sub.t], affect each other with distributed lags. The relationship between those variables can be captured by a VAR model. In this case it is possible to have that a) [Y.sub.t] causes [X..sub.t], b) [X.sub.t] causes [Y.sub.t], c) there is bi-directional feedback (causality among the variables), and finally d) the two variables are independent. The problem is to find an appropriate procedure that allows us to test and statistically detect the cause and effect relationship among the variables.

I) The Granger causality test

Granger (1969) developed a relatively simple test that defined causality as follows: a variable

[Y.sub.t] is said to Granger-cause [X.sub.t] t, if [X.sub.t] can be predicted with greater accuracy by using past values of the [Y.sub.t] variable rather than not using such past values, all other terms remaining unchanged. The Granger causality test for the case of two explanatory variables [Y.sub.t] and [X.sub.t], involves as a first step the estimation of the following VAR model:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (1)

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (2)

Where it is assumed that both [e.sub.2t] and [e.sub.1t] are uncorrelated white-noise error terms. In this model we can have the following different cases:

Case 1: the lagged x terms in (1) may be statistically different from zero as a group, and the lagged y terms in (2) not statistically different from zero. In this case we have that [x.sub.t] causes [y.sub.t]

Case 2: the lagged y terms in (2) may be statistically different from zero, and the lagged x terms in (1) is not statistically different from zero. In this case we have that [y.sub.t] causes [x.sub.t].

Case 3: both sets of x and y terms are statistically different from zero in (1) and (4.11), so that we have bi-directional causality.

Case 4: both sets of x and y terms are not statistically different from zero in (1) and (1), so that [x.sub.t] is independent of [y.sub.t].

The Granger causality test, then, involves the following procedure. First, estimate the VAR model given by equations (1) and (2). Then check the significance of the coefficients and apply variable deletion tests first in the lagged x terms for equation (1), and then in the lagged y terms in (2). According to the results of the variable deletion tests we may conclude about the direction of causality based upon the four cases mentioned above.

Empirical results:

Here, in this section we will deal with both the long--run run and short- run impact of exchange rate on inflation rate i.e. whether exchange rate has any impact on inflation rate in the long--run or in the short- run.

Long-Run Analysis:

We used Engle-Granger procedure to see the impact of exchange rate on inflation rate. But we found the problem of heteroskedasticity. We also applied ARDL procedure but still the problem was high and insignificant elasticity. To check this problem we checked for problem of heteroskedasticity through LM statistic. Then we shifted to Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model for determining long-run impact of exchange rate on the inflation rate.

By testing through GARCH we found that there exists no long-run impact of exchange rate on inflation rate as the residual term must be negative and significant in Error Correction

Model. It was negative but not significant. This showed that there is no long-run relationship between exchange rate and inflation rate which means that exchange rate does not have any impact on inflation rate. The studies reviewed showed that there is positive and significant impact of exchange rate on inflation rate. But the studies we reviewed are of other countries. Very few studies are conducted on relationship of exchange rate with inflation rate in Indian economy.

Table 1 is showing GARCH results but for testing the long--run impact we will apply Error Correction Model (ECM). The residual term in ECM must be negative and significant. The results in ECM are shown in Table 2.

Table 2 is giving us the results of long-run results. The residual term is negative but not significant indicating that there is no long--run impact of exchange rate on inflation rate.

Short- run Analysis

There is no long--run impact of exchange rate on inflation rate so we switched to Granger Causality Test to find short- run impact of exchange rate on inflation rate. Table 3 is showing short- run analysis of relationship between exchange rate and inflation rate. Table 3 is indicating that exchange rate is not impacting inflation rate in short- run but inflation rate has impact on exchange rate in short- run. The p-value is significant at 5% level of significance when we see impact of inflation rate on exchange rate. The relationship between the two is unidirectional.

Thus, the results in table 3 are indicating that there is not even short- run impact of exchange rate on inflation rate. But inflation rate has impact on exchange rate in short- run. The relationship thus is unidirectional.

Summary and conclusions

This paper has empirically examined the impact of exchange rate on inflation rate. The estimated GARCH model took into consideration exchange rate and WPI (as an indicator of inflation) data from 1991 (Q1) to 2009 (Q2). The findings show that there no long- run impact of exchange rate on inflation rate. The residual term is negative but not significant indicating that there is no long--run impact of exchange rate on inflation rate. For finding short- run relationship we applied Granger Causality Test which indicates that there is unidirectional relationship between exchange rate and inflation rate. Exchange rate has no impact on inflation rate in the short- run but inflation rate has short- run impact on exchange rate. This is indicator of that monetary and fiscal policies can help in improving exchange rate conditions in short- run. Our study contradicts with the other studies which showed that there is positive and significant impact of exchange rate on inflation rate. This can be due to the fact that the studies reviewed are of countries other than India. Indian economy data shows that there is no impact of exchange rate on inflation rate both in the short- run and the long--run.

The effects of the uncertainties caused by the volatility in exchange rates and inflation on macroeconomic variables have been subject to extensive theoretical and empirical research. Empirical findings indicate significant impact of exchange rate uncertainty on macroeconomic variables such as output, trade and investment. Similarly, inflation uncertainty appears to affect variables such as output, employment and interest rates. The relationship between exchange rate and inflation uncertainties, on the other hand, has been largely overlooked in the literature. This paper attempts to investigate the impact of exchange rate on inflation rate in India.

India in the past has mostly been a closed economy, following protectionist policies of development. Foreign exchange management and control has been an important tool of lending protection to the domestic economy, checking capital flight, maintaining a comfortable reserve of valuable foreign exchange for development needs and import of essential goods and encouraging exports. To attain these objectives India followed a 'fixed exchange rate' system till the economic crisis of 1991.

The Indian forex market had been heavily controlled since the 1950s, along with increasing trade controls designed to foster import substitution. Consequently, both the current and capital accounts were closed and foreign exchange was made available by the RBI through a complex licensing system. India's post-independence development strategy was both inward-looking and highly interventionist, consisting of import protection, complex industrial licensing requirements, financial repression, and substantial public ownership of heavy industry. However, macroeconomic policy sought stability through low monetary growth and moderate public sector deficits. The current account was in surplus for most years until 1950, and there was a reasonable cushion of official reserves.

Since 1950, India ran continued trade deficits that increased in magnitude in the 1960s. Furthermore, the government of India had a budget deficit problem and could not borrow money from abroad or from the private corporate sector; due to that sector's negative savings rate. As a result, the government issued bonds to the RBI, which increased the money supply.

In 1966, foreign aid was finally cut off and India was told it had to liberalise its restrictions on trade before foreign aid would again materialize. The response was devaluation accompanied by liberalization.

The foreign Exchange Regulation Act (1973) was consolidated and amended in 1947 to regulating certain payments, dealings in foreign exchange and securities, transactions indirectly affecting foreign exchange and the import and export of currency, for the conservation of the foreign exchange resources of the country and the proper utilization thereof in the interests of the economic development of the country.

During the first half of the 1980s, the current account deficit stayed below one and a half percent of GDP. While export growth was slow, the trade deficit was kept in check, as a rapid rise in domestic petroleum production permitted savings on energy imports. At the same time, the high proportion of concessioner external financing kept debt service down.

In the second half of the 1980s, current account deficits widened. India's development policy emphasis shifted from import substitution towards export-led growth, supported by measures to promote exports and liberalize imports for exporters. The government began a process of gradual liberalization of trade, investment, and financial markets. Import and industrial licensing requirements were eased and tariffs replaced some quantitative restrictions. Export growth was rapid, due to the initial measures of deregulation and improved competitiveness associated with the real depreciation of the rupee.

By 1990-91, India was increasingly vulnerable to shocks as a result of its rising current account deficits and greater reliance on commercial external financing. Exports markets were weak in this period leading up to India's crisis, as world growth declined steadily from 4/4 percent in 1988 to 2% percent in 1991. The decline was even greater for U.S. growth, India's single largest export destination. U.S. growth fell from 3.9 percent in 1988 to 0.8 percent in 1990 and to -1 percent in 1991. Consequently, India's export volume growth slowed to 4 percent in 1990-91.

India's balance of payments in 1990-91 also suffered from capital account problems due to a loss of investor confidence. The widening current account imbalances and reserve losses contributed to low investor confidence, which was further weakened by political uncertainties and finally by a downgrade of India's credit rating by the credit rating agencies. The government's economic policies changed drastically in that year, the 1991 liberalization was an extension of earlier, albeit slower, reform efforts that had begun in the 1970s when India relaxed restrictions on imported capital goods as part of its industrialization plan. The Import-Export Policy of 1985-1988 replaced import quotas with tariffs. After 1991, the Government of India further reduced trade barriers by lowering tariffs on imports. In the postliberalization era, quantitative restrictions have not been significant. At that time Indian economy was opened up and foreign exchange regulations were significantly relaxed, the exchange rate fluctuated according to market demand and supply caused by international trade and capital flows to and from India and other speculative forces.

In July 1991 the Indian government devalued the rupee by between 18 and 19 percent. The government also changed its trade policy from its highly restrictive form to a system of freely tradable EXIM scrips which allowed exporters to import 30 percent of the value of their exports (Gupta, pp.73-74)

The task facing India in the early 1990s was, therefore, to gradually move from total control to a functioning forex market. The move towards a market--based exchange rate regime in 1993 and the subsequent adoption of current account convertibility were the key measures in reforming the Indian foreign exchange market.

Reforms in the foreign exchange market focused on market development with prudential safeguards without destabilizing the market. Authorized Dealers of foreign exchange have been allowed to carry on a large range of activities. Banks have been given large autonomy to undertake foreign exchange market, a large number of products have been introduced and entry of newer players has been allowed in the market.

The Indian approach to opening the external sector and developing the foreign exchange market in a phased manner from current account convertibility to the ongoing process of capital account opening is perhaps the most striking success relative to other emerging market economies.

Chronology of India's exchange rate policies

* 1947 (When India became member of IMF): Rupee tied to pound

* 18 September, 1949: Pound devalued; India maintained par with pound

* 6 June, 1966: Rupee is devalued

* 18 November, 1967: UK devalued pound, India did not devalue

* August 1971: Rupee pegged to gold/dollar, international financial crisis

* 18 December, 1971: Dollar is devalued

* 20 December, 1971: Rupee is pegged to pound sterling again

* 1971-1979: The Rupee is overvalued due to India's policy of import substitution

* 23 June, 1972: UK floats pound, India maintains fixed exchange rate with pound

* 1975: India links rupee with basket of currencies of major trading partners. Although the basket is periodically altered, the link is maintained until the 1991 devaluation.

* July 1991: Rupee devalued by 18-19 %

* March 1992: Dual exchange rate, LERMS, Liberalised Exchange Rate Management System

* March 1993: Unified exchange rate: $1 = Rs 31.37

* 1993/1994: Rupee is made freely convertible for trading, but not for investment purposes

In the present study, an attempt has been made to evaluate the impact of exchange rate on India's inflation rate. Section I presents the link behind exchange rate and inflation rate. Section II gives the survey of literature related to foreign exchange rate and inflation rate. In Section III of the paper data source and methodology of the study has been dealt with. Section IV attempts to statistically evaluate the impact of exchange rate on India's inflation rate. In the last section V, summary and conclusions of the study has been given.

The effects of the uncertainties caused by the volatility in exchange rates and inflation on macroeconomic variables have been subject to extensive theoretical and empirical research.

Exchange rate system has an important role in reducing or minimizing the risk of fluctuations in exchange rates, which will have an impact on the economy. In the system of floating exchange rates, exchange rate fluctuations can have a strong impact on the level of prices through the aggregate demand (AD) and aggregate supply (AS). On the aggregate supply, depreciation (devaluation) of domestic currency can affect the price level directly through imported goods that domestic consumers pay. However, this condition occurs if the country is the recipient countries of international prices (international price taker). Non direct influence from the depreciation (devaluation) of currency against the price level of a country can be seen from the price of capital goods (intermediate goods) imported by the manufacturer as an input. The weakening of exchange rate will cause the price of inputs more expensive, thus contributing to a higher cost of production. Manufacturers will certainly increase the cost to the price of goods that will be paid by consumers. As a result, the price level aggregate in the country increases or if it continues it will cause inflation.

Exchange rate movements can influence domestic prices via effect on aggregate supply and demand. On the supply side, exchange rates could affect prices paid by the domestic buyers of imported goods directly. In an open economy (an international price taker), when the currency depreciates it will result in higher import prices and vice-versa. Exchange rate fluctuations could have an indirect supply effect on domestic prices. The potentially higher cost of imported inputs associated with an exchange rate deprecation increases marginal cost and leads to higher prices of domestically produced goods (Hyder and Shah, 2004). Further import-competing firms might increase prices in response to an increase in foreign competitor in order to improve profit margins. The extent of such price adjustment depends on a variety of factors such as market structure, nature of government exchange rate policy, or product substitutability.

Exchange rate fluctuations can also affect aggregate demand. To a certain extent, exchange rate depreciations (appreciations) increase (decrease) foreign demand for domestic goods and services, causing increase (decrease) in net exports and hence aggregate demand (Hyder and Shah, 2004). This may increase real output. Furthermore, the expansion in domestic demand and gross national product may bid up input prices and accelerate wage demands by workers seeking higher wages to maintain real wages. The nominal wage rise may result in further price increases.

Review of Literature

Study Author The Empirical Jyh-Lin Wu (1996) Investigation Of Long-Run Purchasing Power Parity: The Case Of Taiwan Exchange Rates Exchange Rates and Patrick Honohan and Philip Inflation under EMU: R. Lane (2004) An Update Granger Non- Jean-Claude Maswana Causality Test of (2006) the Inflation- Exchange Rate in the Democratic Congo The impact of Real Nyugen Thi Thuy Vinh exchange Rate on and Seiichi Fujita (2007) Output and Inflation in Veitnam: A VAR approach Exchange Rate Pass- Michele Ca' Zorzi, Elke Through in Emerging Hahn and Marcelo Sanchez Markets (2007) Do Exchange Rate George Chouliarakis and Regimes Matter for PKG Harischandra (2008) Inflation Persistence? Theory and Evidence from the History of UK and US Inflation Uncertainty Murat Tacdemir and Murat Spillovers between Aslan (2009) Exchange Rates and Inflation: Evidence from Turkey Inflation and Darine Ghanem Lameta Exchange Rate (2010) Regimes: Evidence from MENA Countries Exchange Rates and Bahram Adrangi and Mary Inflation Rates: E. Allender (2010) Exploring Nonlinear Relationships The Relationship Noer Azam Achsani, Arie between Inflation Jayanthy F A Fauzi and and Real Exchange Piter Abdullah (2010) Rate: Comparative Study between ASEAN+3, the EU and North America Exchange Rate B Imimole and A Enoma Depreciation and (2011) Inflation in Nigeria (1986-2008) Investigating Siok Kun Sek, Cheau the Relationship Pian Ooi and Mohd. Tahir between Exchange Ismail (2012) Rate and Inflation Targeting The Exchange Rate- Glenville Rawlins Inflation Link: The Experience of Some Caribbean and Central American Countries Study Methodology The Empirical Co-integration Investigation Of Test Long-Run Purchasing Power Parity: The Case Of Taiwan Exchange Rates Exchange Rates and Generalized Inflation under EMU: Least Squares An Update Granger Non- Granger Casuality Causality Test of Test the Inflation- Exchange Rate in the Democratic Congo The impact of Real VAR Approach exchange Rate on Output and Inflation in Veitnam: A VAR approach Exchange Rate Pass- VAR Model Through in Emerging Markets Do Exchange Rate Structural Stability Regimes Matter for Tests Inflation Persistence? Theory and Evidence from the History of UK and US Inflation Uncertainty GARCH Model Spillovers between Exchange Rates and Inflation: Evidence from Turkey Inflation and Panel data Model Exchange Rate Regimes: Evidence from MENA Countries Exchange Rates and GARCH Model Inflation Rates: Exploring Nonlinear Relationships The Relationship Panel Data model between Inflation with Fixed effects and Real Exchange Rate: Comparative Study between ASEAN+3, the EU and North America Exchange Rate ARDL Depreciation and Cointegration Inflation in Nigeria Procedure (1986-2008) Investigating GARCH Models the Relationship between Exchange Rate and Inflation Targeting The Exchange Rate- Johanssen Inflation Link: The Cointegration Experience of Some Techniques Caribbean and Central American Countries Study Results The Empirical Long-run equilibrium relationships Investigation Of between exchange rates and Long-Run Purchasing consumer price indices. Power Parity: The Case Of Taiwan Exchange Rates Exchange Rates and The strength of the US dollar had Inflation under EMU: an important impact on inflation An Update divergence. Granger Non- Granger causality between inflation Causality Test of and the exchange rate is the Inflation- bidirectional in the short-run, but in Exchange Rate in the the long--run only exchange rate Democratic Congo causes inflation without a feedback effect. The impact of Real Real devaluation has positive exchange Rate on impact on both output and inflation. Output and Inflation in Veitnam: A VAR approach Exchange Rate Pass- Positive relation between Through in Emerging Exchange Rate Pass Through and Markets inflation. Do Exchange Rate Inflation persistence can vary with Regimes Matter for exchange rate regimes. Inflation Persistence? Theory and Evidence from the History of UK and US Inflation Uncertainty The inflation uncertainty contributes Spillovers between significantly to the nominal Exchange Rates and exchange rate uncertainty. In the Inflation: Evidence managed exchange rate period, from Turkey there is no evidence of uncertainty spillover between the nominal exchange rates and inflation. Inflation and Strong relationship between the Exchange Rate choice of exchange rate regime Regimes: Evidence and inflation from MENA Countries Exchange Rates and Explains nonlinearities between Inflation Rates: monthly exchange rates and Exploring Nonlinear inflation rates. Relationships The Relationship There is a strong correlation between Inflation between the movements of inflation and Real Exchange with real exchange rate in most Rate: Comparative countries to be analyzed. For Asia, Study between there is a significant one-way ASEAN+3, the EU and causal relationship, where the North America nominal and real exchange rates have a significant impact on the rate of inflation. Exchange Rate Depreciation has positive and long Depreciation and --run effect on exchange rate. Inflation in Nigeria (1986-2008) Investigating Inflation Targeting leads to higher the Relationship volatility in exchange rate between Exchange movement in majority economies. Rate and Inflation Significant correlation between Targeting exchange rate movements and inflation and output movements in both sub-periods. The Exchange Rate- The existence of a stable long term Inflation Link: The relationship between current Experience of Some inflation differentials and the Caribbean and bilateral nominal exchange rate for Central American each country. Countries

The above studies show mixed and inconclusive results on the relationship between exchange rate and inflation. Most of the studies reveal that there is positive relation between exchange rate depreciation and inflation rate. It has also been seen that the research on impact of exchange rate and inflation is largely overlooked in the Indian Economy.

Data Source and Methodology:

Data Source:

To achieve the stated objective of the paper, secondary data has been used. Major data source is Economic Survey of India, especially on Database on Indian economy. The time period of the study is 1991 Q1 to 2009 Q2. For theoretical explanation standard work on the subject including internet has been consulted.

For finding empirical results eviews has been used.

Methodology:

The first step is to test for stationarity of the series with the help of unit root tests. In the presence of non-stationary variables, there is possibility of spurious regression. The second step is to test for cointegration if the variables are non-stationary in their levels and stationary in first difference. Once the cointegration has been established amongst the variables, the third step is to formulate Error-Correction Model (ECM) to examine the casual relationship between exchange rate and inflation rate.

For correcting the problem of autoregression and heteroscedasticity we have used GARCH model for finding long-run impact. Generalised Autoregressive Conditional Heteroskedasticity (GARCH) models are specifically designed to model and forecast conditional variances. The variance of the dependent variable is modeled as a function of past values of the dependent variable and independent, or exogenous variables.

The GARCH (1,1) Model:

GARCH (1,1) model specifications:

Yt = X't [sup.[theta]] + [[member of].sub.t] (1)

[[sigma].sup.2.sub.t] = [omega] + [alpha] [[member of].sup.2.sub.t-1] + [beta] [[sigma].sup.2.sub.t-1] (2)

in which the mean equation given in equation 1 is written as a function of exogenous variables with an error term. Since is the one-period ahead forecast variance based on past information, it is called conditional variance. The conditional variance equation is specified in equation 2 is a function of three terms:

The constant term [omega]

News about volatility from the previous period, measured as the lag of the squared residual from the mean equation [[member of].sup.2.sub.t-1] (the ARCH term). Last period's forecast variance [[sigma].sup.2.sub.t-1] (the GARCH term).

The (1, 1) in GARCH (1, 1) refers to the presence of a first-order autoregressive GARCH term (the first term in parentheses) and a first-order moving average ARCH term (the second term in parentheses). An ordinary ARCH model is a special case of a GARCH specification in which there are no lagged forecast variances in the conditional variance equation--i.e., a GARCH (0, 1).

This specification is often interpreted in a financial context, where an agent or trader predicts this period's variance by forming a weighted average of a long term average (the constant), the forecasted variance from last period (the GARCH term), and information about volatility observed in the previous period (the ARCH term). If the asset return was unexpectedly large in either the upward or the downward direction, then the trader will increase the estimate of the variance for the next period. This model is also consistent with the volatility clustering often seen in financial returns data, where large changes in returns are likely to be followed by further large changes. There are two equivalent representations of the variance equation that may aid in interpreting the model:

* If we recursively substitute for the lagged variance on the right-hand side of equation 2 we can express the conditional variance as a weighted average of all of the lagged squared residuals:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (3)

[[sigma].sup.2.sub.t]

We see that the GARCH(1,1) variance specification is analogous to the sample variance, but that it down-weights more distant lagged squared errors.

* The error in the squared returns is given by [V.sub.t] [[epsilon].sup.2] - [[sigma].sup.2.sub.t]. Substituting for the variances in the variance equation and rearranging terms we can write our model in terms of the errors:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (4)

Thus, the squared errors follow a heteroskedastic ARMA(1,1) process. The autoregressive root which governs the persistence of volatility shocks is the sum of a plus p in many applied settings, this root is very close to unity so that shocks die out rather slowly.

Testing for causality

One of the good features of VAR models is that they allow us to test for the direction of causality. Causality in econometrics is somewhat different to the concept in everyday use; it refers more to the ability of one variable to predict (and therefore cause) the other. Suppose two variables, say [Y.sub.t] and [X.sub.t], affect each other with distributed lags. The relationship between those variables can be captured by a VAR model. In this case it is possible to have that a) [Y.sub.t] causes [X..sub.t], b) [X.sub.t] causes [Y.sub.t], c) there is bi-directional feedback (causality among the variables), and finally d) the two variables are independent. The problem is to find an appropriate procedure that allows us to test and statistically detect the cause and effect relationship among the variables.

I) The Granger causality test

Granger (1969) developed a relatively simple test that defined causality as follows: a variable

[Y.sub.t] is said to Granger-cause [X.sub.t] t, if [X.sub.t] can be predicted with greater accuracy by using past values of the [Y.sub.t] variable rather than not using such past values, all other terms remaining unchanged. The Granger causality test for the case of two explanatory variables [Y.sub.t] and [X.sub.t], involves as a first step the estimation of the following VAR model:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (1)

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (2)

Where it is assumed that both [e.sub.2t] and [e.sub.1t] are uncorrelated white-noise error terms. In this model we can have the following different cases:

Case 1: the lagged x terms in (1) may be statistically different from zero as a group, and the lagged y terms in (2) not statistically different from zero. In this case we have that [x.sub.t] causes [y.sub.t]

Case 2: the lagged y terms in (2) may be statistically different from zero, and the lagged x terms in (1) is not statistically different from zero. In this case we have that [y.sub.t] causes [x.sub.t].

Case 3: both sets of x and y terms are statistically different from zero in (1) and (4.11), so that we have bi-directional causality.

Case 4: both sets of x and y terms are not statistically different from zero in (1) and (1), so that [x.sub.t] is independent of [y.sub.t].

The Granger causality test, then, involves the following procedure. First, estimate the VAR model given by equations (1) and (2). Then check the significance of the coefficients and apply variable deletion tests first in the lagged x terms for equation (1), and then in the lagged y terms in (2). According to the results of the variable deletion tests we may conclude about the direction of causality based upon the four cases mentioned above.

Empirical results:

Here, in this section we will deal with both the long--run run and short- run impact of exchange rate on inflation rate i.e. whether exchange rate has any impact on inflation rate in the long--run or in the short- run.

Long-Run Analysis:

We used Engle-Granger procedure to see the impact of exchange rate on inflation rate. But we found the problem of heteroskedasticity. We also applied ARDL procedure but still the problem was high and insignificant elasticity. To check this problem we checked for problem of heteroskedasticity through LM statistic. Then we shifted to Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model for determining long-run impact of exchange rate on the inflation rate.

By testing through GARCH we found that there exists no long-run impact of exchange rate on inflation rate as the residual term must be negative and significant in Error Correction

Model. It was negative but not significant. This showed that there is no long-run relationship between exchange rate and inflation rate which means that exchange rate does not have any impact on inflation rate. The studies reviewed showed that there is positive and significant impact of exchange rate on inflation rate. But the studies we reviewed are of other countries. Very few studies are conducted on relationship of exchange rate with inflation rate in Indian economy.

Table 1 is showing GARCH results but for testing the long--run impact we will apply Error Correction Model (ECM). The residual term in ECM must be negative and significant. The results in ECM are shown in Table 2.

Table 2 is giving us the results of long-run results. The residual term is negative but not significant indicating that there is no long--run impact of exchange rate on inflation rate.

Short- run Analysis

There is no long--run impact of exchange rate on inflation rate so we switched to Granger Causality Test to find short- run impact of exchange rate on inflation rate. Table 3 is showing short- run analysis of relationship between exchange rate and inflation rate. Table 3 is indicating that exchange rate is not impacting inflation rate in short- run but inflation rate has impact on exchange rate in short- run. The p-value is significant at 5% level of significance when we see impact of inflation rate on exchange rate. The relationship between the two is unidirectional.

Thus, the results in table 3 are indicating that there is not even short- run impact of exchange rate on inflation rate. But inflation rate has impact on exchange rate in short- run. The relationship thus is unidirectional.

Summary and conclusions

This paper has empirically examined the impact of exchange rate on inflation rate. The estimated GARCH model took into consideration exchange rate and WPI (as an indicator of inflation) data from 1991 (Q1) to 2009 (Q2). The findings show that there no long- run impact of exchange rate on inflation rate. The residual term is negative but not significant indicating that there is no long--run impact of exchange rate on inflation rate. For finding short- run relationship we applied Granger Causality Test which indicates that there is unidirectional relationship between exchange rate and inflation rate. Exchange rate has no impact on inflation rate in the short- run but inflation rate has short- run impact on exchange rate. This is indicator of that monetary and fiscal policies can help in improving exchange rate conditions in short- run. Our study contradicts with the other studies which showed that there is positive and significant impact of exchange rate on inflation rate. This can be due to the fact that the studies reviewed are of countries other than India. Indian economy data shows that there is no impact of exchange rate on inflation rate both in the short- run and the long--run.

Table 1: Garch Model Coefficient Std. Error C 6.212838 0.688132 LNEX -2.554002 0.256425 Variance Equation C 0.109239 0.057231 RESID[(-1).sup.^2] 0.789807 0.427019 Garch (-1) 0.144997 0.191387 R--squared 0.526272 mean dependent var Adjusted R--squared 0.498809 S. D. dependent var S. E. of regression 1.288366 Akaike infor criterion Sum squared resid 114.5322 Schwarz criterion Log likelihood -81.35818 F-statistic Durbin -Wastson stat 0.316628 Prob. (F-statistic) Z-Statistic Prob. C 9.028551 0.0000 LNEX -9.960052 0.0000 Variance Equation C 1.908742 0.0563 RESID[(-1).sup.^2] 1.849580 0.0644 Garch (-1) 0.757613 0.4487 R--squared -1.048580 Adjusted R--squared 1.819859 S. E. of regression 2.334005 Sum squared resid 2.489685 Log likelihood 19.16329 Durbin -Wastson stat 0.000000 Table 2: Error Correction Model (ECM) Variable Coefficient Std. Error C 0.003193 0.013306 D(LNWPI (-1) 0.029859 0.139340 D(LNWPI (-2) 0.054792 0.139379 D(LNWPI (-3) 0.019976 0.139501 D(LNWPI (-1) 0.038805 0.109931 D(LNWPI (-2) 0.051250 0.108573 D(LNWPI (-3) 0.027063 0.106323 R--squared 0.025188 mean dependent var Adjusted R--squared -0.084872 S. D. dependent var S. E. of regression 0.110187 Akaike info criterion Sum squared resid 0.752755 Schwarz criterion Log likelihood 59.31220 F-statistic Durbin -Wastson stat 1.995753 Prob. (F-statistic) Variable T-Statistic Prob. C 0.239964 0.8111 D(LNWPI (-1) 0.214286 0.8310 D(LNWPI (-2) 0.393113 0.6956 D(LNWPI (-3) 0.143193 0.8866 D(LNWPI (-1) 0.352995 0.7253 D(LNWPI (-2) 0.472033 0.6386 D(LNWPI (-3) 0.254538 0.7999 R--squared 0.003586 Adjusted R--squared 0.105789 S. E. of regression -1.466063 Sum squared resid -1.209092 Log likelihood 0.228856 Durbin--Wastson stat 0.976880 Table 3: Granger Causality Test Sample : 1991 Q1 and 2009 Q 2 Lags: 1 Null Hypothesis Obs F-Statistic Probability LNEX does not Granger Cause LNWPI 73 0.58300 0.44770 LNWPI does not Granger Cause LNEX 4.70994 0.03338

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Author: | Payal; Makkar, Suman |
---|---|

Publication: | Political Economy Journal of India |

Geographic Code: | 9INDI |

Date: | Jan 1, 2012 |

Words: | 4752 |

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