INTRODUCTION
The performance of an economy is largely affected by monetary, fiscal and exchange
rate policies. These policies establish the growth of public and private sector
in the economy and determine the successive investment patterns. The monetary
policies and macroeconomic events effect the general economic actions. There
has been evidence of significant linkages between the macroeconomic variables
of the country in the studies of Fama and French (1989),
Schwert (1990) and Black et al.
(1997).
Gross Domestic Product (GDP), inflation rate and unemployment are three extensively
noticed macroeconomic variables of economic activity. The other important macroeconomic
variables include Interest Rate, International trade balance and Productivity.
These variables impact the growth, employment and the inflation rate in the
country. GDP is considered as the main indicator of economic movement. The periodic
change in GDP shows the growth rate of overall economic output. The periodic
changes i.e., Monthly, Quarterly, Yearly of GDP growth can be reasonably unpredictable.
The Inventory and net export swings are the major variables, which affects the
GDP volatility (MACROECONOMICS, World Scientific Publishing Co. Pvt. Ltd.).
A good amount of research has concentrated on finding out the impact of macroeconomic
variables on the stock prices in various countries. Chen
et al. (1986), Mukherjee and Naka (1995), Liljeblom
et al. (1997), Maysami and Koh (2000), Arora
and Vamvakidis (2001), Paul and Mallik (2001), Sharma
and Wongbangpo (2002), Blomberg et al. (2004),
Patra and Poshakwale (2006), Humpe
and Macmillan (2005), Bialkowski et al. (2008),
Liu and Shrestha (2008), Chiang
and Kee (2009), Hasan and Javed (2009) and Aizenman
and Noy (2009) attempted to observe the relationship between the stock returns
and the macroeconomic variables such as interest rates, inflation and real
activity. However, there have been few researches that have concentrated on
observing the comovement between the macroeconomic variables interest. This
study attempted to comment on the causal linkages and the comovement of different
macroeconomic variables. The study of this comovement is important because
it would give us an insight as to whether the variables would usually move in
the same direction and if those do not move in the same direction, which variables
have a larger impact on the GDP growth, the most reputed measure of economic
performance.
Figure 1 depicts the comparison of real GDP growth of world,
advance and emerging economies. The world GDP is the document of the World Bank
in which the GDP calculated is the combined GDP for all countries. The GDP of
advance economies is the combined GDP of the countries which are developed i.e.,
USA, UK, Germany and France. The combined GDP of the countries, i.e., India,
Russia, Brazil, Hong Kong, Singapore, China refers to the Emerging economies’
GDP. Further the figure shows that the GDP growth of the emerging and developing
countries is better than the GDP growth of the advanced countries as well as
the growth of the world GDP. The figure depicts that in the year 2009 the GDP
growth of the world as well as the advance economies was negative while the
GDP growth of the emerging and developing countries was in the positive. The
figure supports the fact that there is a need of research on the linkages between
the macroeconomic variables in the developing countries. India and Sri Lanka
are two South Asian Nations that count among the developing economies of the
world. However, India is significantly larger than Sri Lanka in terms of various
geographical and economic factors. In terms of Economy, Indian economy stands
at $1.846 trillion, which is around 31 times more than that of Sri Lankan Economy
that values $59 Billion (September 2011, IMF, World Economic Outlook). In this
way, the two countries are significantly different from each other in terms
of economic and geographical figures.

Fig. 1: 
The real GDP growth comparison of the world, advance and
emerging economies 
Attempting to find out if various macroeconomic indicators move in tandem
with each other in two countries that are so different in their size would provide
a good insight as regards the issue. Therefore, the study focuses on understanding
the extent of comovement of macroeconomic variables in India and Sri Lanka.
The paper studies the pattern of macroeconomic variables including Consumer Price Index (CPI), Wholesale Price Index (WPI), Gross Domestic Product (GDP), Gross National Income(GNI) and Rate of interest in India and Sri Lanka for the year 20022009 and analyzes the impact of these variables on the GDP growth in India visàvis Sri Lanka. While CPI is a measure of the average change over time in the prices paid by urban consumers for a market basket of consumer goods and services (Bureau of Labor Statistics), WPI is the price of a representative basket of wholesale goods and GNI consists of: the personal consumption expenditures, the gross private investment, the government consumption expenditures, the net income from assets abroad (net income receipts) and the gross exports of goods and services, after deducting two components: the gross imports of goods and services and the indirect business taxes.
Numerous studies investigate the relationship between the stock returns, interest
rates, inflation and real activity. Chen et al. (1986)
investigated the systematic event influence on US stock market return by several
economic variables. Aizenman and Noy (2009) investigate
the impact of natural disasters on GDP. Mukherjee and Naka
(1995) examine relationship between the Japanese stock market and several
macroeconomic variables. Liljeblom et al. (1997)
studied the impact of macroeconomic variables on the stock price in Finland.
Maysami and Koh (2000) investigates the inter linkage
amongst macroeconomic variables (exchange rate, long and short term interest
rates, inflation, money supply, domestic exports and industrial production)
and Stock market of Singapore. Arora and Vamvakidis (2001)
investigated the impact of US growth on other countries. Paul
and Mallik (2001) explored the long run relationship among macroeconomic
factors and equity prices in Australian banking and finance sector. Sharma
and Wongbangpo (2002) investigated the interaction of stock price and macroeconomic
variables in five ASEAN countries stock market. Blomberg
et al. (2004) evaluated the effect of wars on GDP. Gan
et al. (2006) evaluated whether the New Zealand stock index reflects
the changes in the analyzed macroeconomic variables. Patra
and Poshakwale (2006) examined the shortrun relationship and the longrun
equilibrium relationship among selected macroeconomic variables and Athens stock
exchange. Humpe and Macmillan (2005) evaluated the effect
of the macroeconomic variables on stock prices in US and Japan. Bialkowski
et al. (2008) examined whether national elections would cause higher
stock market volatility. Liu and Shreshta (2008) investigated
the relationship between the Chinese stock market indices and a set of macroeconomic
variables, including money supply, industrial production, inflation, exchange
rate and interest rates. Hasan and Javed (2009) explored
the longterm dynamic relationship between equity prices and monetary variables
in Pakistan.
Mixed results have been produced by the past research. Chen
et al. (1986), Liu and Shrestha (2008) and Hasan
and Javed (2009) reveal the existence of a longterm relationship between
the equity market and monetary variables, such as, money supply, treasury bill
rates, foreign exchange rates and the consumer piece index. Aizenman
and Noy (2009) revealed that there was a negative GDP growth rate during
the period of disaster. Mukherjee and Naka (1995) found
a positive relationship between share price and money supply accompanied by
exchange rate and industrial production. Liljeblom et
al. (1997) indicated a predictive power from stock market volatility
to macroeconomic volatility in Finland was higher than US. Maysami
and Koh (2000) used the vector correlation model on the data of variables
from 1988 to 1995 and unearth that the change in the macroeconomic variables
affects the Singapore stock market, thus there is a cointegration relation exist
between the two. Arora and Vamvakidis (2001) found that
the impact significant considering the United States is a global trading partner.
Paul and Mallik (2001) reveal that interest rate has
a significant negative effect on equity prices and GDP growth has a significant
a positive effect on the equity prices of banking and finance sector. However,
no significant effect of inflation is observed on equity prices. Sharma
and Wongbangpo (2002) observed that the past macroeconomic variables in
the ASEAN countries were able to predict future changes in the stock price indices.
Blomberg et al. (2004) found that the outbreak
of external war or internal conflict has a significant negative impact on real
GDP growth in the year of the event and terrorist attacks have a smaller but
nonetheless significant negative impact. Patra and Poshakwale
(2006) concluded about presence of both shortterm and longterm relationship
between inflation, money supply and trading volumes and the stock prices. Conversely,
no relationship was found between exchange rate and stock prices. Humpe
and Macmillan (2005) revealed that the data for US are consistent with a
single cointegrating vector, where stock prices are positively related to industrial
production and negatively related to both the consumer price index and longterm
interest rate. They also find an insignificant (although positive) relationship
between US stock prices and the money supply. Bialkowski
et al. (2008) revealed that the highly volatility and correlation between
stock returns and elections.
The current study contributes to the literature in numerous ways. First, this
is the study concentrating on the developing yet differently poised economies
of India and Sri Lanka. Second, this research studies the linkages between the
macroeconomic variables rather than the impact of macroeconomic variables
on the stock market activity (as has been the case with previous studies). Third,
it studies the linkages within the developing economies rather than with the
developed world. Fourth, it uses a combination of the various methods used empirically
to analyze the data.
MATERIALS AND METHODS
Data used for analysis: This study uses the monthly data from 2002 onwards to 2009 has been used in case of all the variables like, GDP (Gross Domestic Product), GNI (Gross National Income), wholesale price index (WPI), consumer price index (CPI), exchange rates, bank rates and balance of payments. The major source of data of all the above macro economic variables is International Monetary Fund online data source. Index Number (2000 = 100) is used as the base index for the whole research data.
Hypothesis of the study: The following hypothesis are formed for the purpose of the study:
(i) 
H_{0} = Macroeconomic variables including CPI, WPI,
GDP, GNI and Rate of interest are independent of each other and hence the
movement of any one variable does not replicate the movement in the other 
(ii) 
H_{0} = The GDP growth rate does not have any impact on the macroeconomic
variables as mentioned in (i) above and 
where, H_{0} is the impact as mentioned in (ii) above is nonexistent
in the case of both India and Sri Lanka.
Tools used for data analyzing: Data have been analyzed using econometric tools. The analysis of econometrics can be performed on a series of stationary nature. In order to check whether or not the series are stationary, the study performs the Augmented DickeyFuller test under the unit root test to finally confirm whether or not the series are stationary. For the basic understanding of Unit root testing, a look at the following equation would help:
where, x_{t} are optional exogenous regressors which may consist of constant, or a constant and trend, ρ and δ are parameters to be estimated and the ε_{t} are assumed to be white noise. If ρ≥1, y is a nonstationary series and the variance of y increases with time and approaches infinity. If ρ<1, y is a (trend)stationary series. Thus, the study evaluates the hypothesis of (trend)stationarity by testing whether the absolute value of ρ is strictly less than one.
The Standard DickeyFuller test is carried out by estimating (Eq. 2) after subtracting y_{t1} from both sides of the equation:
where, α = ρ1. The null and alternative hypotheses may be written as:
In order to make the series stationary, the paper takes the log of the six series and arrive at the daily return of the six series. All the remaining analysis is performed at the daily return (log of the series) of the six exchanges. These variables are names as dindia, dsrilanka, dbangladesh, dpakistan, dnepal and dmaldives.
At the stationary log series of the six stock exchanges, the paper performs the Granger’s causality model in order to observe whether the return at each stock exchange granger causes the return at the remaining five stock exchanges.
The (Granger and Swanson, 1996) approach to the question
of whether x causes y is to see how much of the current y can be explained by
past values of y and then to see whether adding lagged values of x can improve
the explanation. y is said to be Grangercaused by x if x helps in the prediction
of y, or equivalently if the coefficients on the lagged x ’s are statistically
significant. It is pertinent to note that twoway causation is frequently the
case; x Granger causes y and y Granger causes x. It is important to note that
the statement “x Granger causes y” does not imply that y is the effect
or the result of x. Granger causality measures precedence and information content
but does not by itself indicate causality in the more common use of the term.
In Granger’s Causality, there are bivariate regressions of the undermentioned
form:
for all possible pairs of (x, y) series in the group. In (Eq. 6), the paper takes lags ranging from 1 to l. In Granger’s model, one can pick a lag length, l that corresponds to reasonable beliefs about the longest time over which one of the variables could help predict the other.
The reported Fstatistics are the Wald statistics for the joint hypothesis:
for each equation. The null hypothesis is that x does not Grangercause y in the first regression and that y does not Grangercause x in the second regression.
The paper follows the application of Granger’s causality with the Vector Autoregression (VAR) Model. The vector autoregression (VAR) is commonly used for forecasting systems of interrelated time series and for analyzing the dynamic impact of random disturbances on the system of variables. The VAR approach sidesteps the need for structural modeling by treating every endogenous variable in the system as a function of the lagged values of all of the endogenous variables in the system. The mathematical representation of a VAR is:
where, y_{t} is a k vector of endogenous variables, x_{t }is a d vector of exogenous variables, A_{1}, …… , A_{p} and B are matrices of coefficients to be estimated and ε_{t} is a vector of innovations that may be contemporaneously correlated but are uncorrelated with their own lagged values and uncorrelated with all of the righthand side variables.
The study also applies the Variance Decomposition Analysis in order to quantify the extent upto which the six indices are influenced by each other. While impulse response functions trace the effects of a shock to one endogenous variable on to the other variables in the VAR, variance decomposition separates the variation in an endogenous variable into the component shocks to the VAR. Thus, the variance decomposition provides information about the relative importance of each random innovation in affecting the variables in the VAR.
RESULTS AND DISCUSSION
Descriptive statistics of the macroeconomic variables: Table
1 shows that the mean of the first four variables, i.e., Exchange Rates,
Bank rates, WPI and CPI is higher in Sri Lanka when compared with India while
the same for the remaining three variables, i.e., GDP, GNI and Balance of Payments
is higher in the case of India.
Table 1: 
Descriptive statistics of the macroeconomic variables 

Table 2: 
Correlation matrix of the macroeconomic variables 

Ind: India, SL: Sri Lanka 
The first four variables being on the higher side gives two important implications(1)
a weaker currency and (2) higher interest rates in Sri Lanka than in India.
The last three variables being higher for India, on the other hand, signal the
strength of the Indian Economy visàvis the Sri Lankan economy. The
comparison of India and Sri Lanka on the front of Standard Deviation and Coefficient
of Variation shows the variables to be higher for Sri Lanka than for India.
This implies that the Sri Lankan economy is more volatile than the Indian economy.
The skewness for all the variables in both the countries being other than 0
and the kurtosis statistic other than 3 signal that the variables form a nonnormal
distribution.
Table 2 shows that in case of Sri Lanka, Exchange rates have
a significant positive relationship with the WPI, CPI, GDP and GNP while for
none of these variables is there a significant relationship in the case of India.
The same results have been produced by Chen et al.
(1986), Liu and Sharma (2008) and Hasan
and Javed (2009) in their research in which the revealed the fact about
there respective countries that there is of a longterm relationship between
the equity market and monetary variables. In the current study the Balance of
Payments is observed to find a negative relationship with most of the other
variables in case of both the countries. Paul and Mallik
(2001) also found the negative relationship between interest rate and equity
prices in the case of Australia.
Table 2 leads us to reject the first Null Hypothesis and implies that the variables under study other than Bank rate and Balance of payments are positively correlated with each other.
Regression with GDP as dependent variable: From Table
3, the study formulates the regression equation Y= a+bX, where in Y is the
dependent variable (GDP) and X is the independent variable (Exchange rates,
Bank rates, WPI, CPI, GNI and Balance of Payments). In the case of India, the
paper arrives at the regression equation GDP = 5371.526+(7.066) Exchange Rates+(568.306)
Bank Rates+(23.592) WPI+(16.876) CPI+(1.079) GNI+(1.298) Balance of Payments.
Similarly for Sri Lanka, the research arrives at the regression equation GDP
= 199957.986+(203.156) Exchange Rates+(4508.881) Bank Rates+(854.678) WPI+(3164.948)
CPI+(0.911) GNI+(0.151) Balance of Payments. The results of regression show
that there is a visible effect of the independent variable Exchange Rates, Bank
Rates, WPI on the GDP in the case of India. Aizenman and
Noy (2009) also revealed the fact in his research that there is a negative
cause effect relationship between war and GDP and the GDP rate decline during
war, has produced the similar kind of result.
Table 3: 
Exchange rates, Bank rates, WPI, CPI, GNI and Balance of
Payments regressed on GDP 

Ind: India, SL: Sri Lanka 
Table 4: 
Exchange rates, bank rates, WPI, CPI, GNI and balance of
payments regressed on GDP 

Table 4 shows the sum of squares, mean square, f statistic
and level of significance for regression equation as also for the residuals.
The first important value that needs to be looked at is the level of significance
the value of which is found to be 0.010 and 0.002 for India and Sri Lanka respectively.
Both of these values are significant at 5% level of significance. Looking at
the sum of squares, the paper finds that the regression equation in both the
countries account for a major proportion of the values of the dependent variable
(GDP). Paul and Mallik (2001) showed in his study that
there is a significant impact of the GDP on the equity prices in the case of
Australia.
For performing the econometric analysis, it is very essential for the researcher to make sure that the series under reference are stationary. In order to make the series stationary, the paper takes log of the three series on which the further analysis shall be performed. In this way, three new variables are created and the study assigns those, names LOGExchange, LOGWPI and LOGCPI which denote the LOG of Exchange rate, WPI and CPI, respectively. Going further, the paper discusses the linkages between the logs of exchange rate, WPI and CPI.
Table 5 presents the descriptive statistics of the series of log Exchange, log WPI and log CPI for India and Sri Lanka.
Table 5 exhibits the descriptive statistics of changes in exchange rates, WPI and CPI respectively. It can be observed from the table that the changes in macroeconomic variables are higher in case of Sri Lanka than those in India. The standard deviation, variance and coefficient of variation are also higher in the case of Sri Lanka than those in India. This signals a higher volatility in the macroeconomic performance of Sri Lanka than that in India.
Econometric analysis of macroeconomic variables: Stationarity tests
are carried out on the variables because to apply econometric analysis, first
the series have to be made stationary. Augmented Dickey Fuller (ADF) test have
been done and after the application of these tests all the series have been
found stationary at various significance levels.
Table 5: 
Descriptive statistics of the changes in exchange rates,
WPI and CPI 

Table 6: 
Unitroot tests for macroeconomic variables 

Table 7: 
Granger causalityexchange rate, WPI and CPI of India 

Table 8: 
Granger causalityexchange rate, WPI and CPI of Sri Lanka 

The unitroot test is performed on the three series in order to test the null
hypothesis that the series has a unit root. The findings of the unitroot test
and the augmented DickeyFuller test are shown below in the following tables.
Table 6 tests the null hypothesis that the series under reference have a unitroot. This null hypothesis implies that the series are nonstationary in nature. The probability value for all the six cases is found to be below 0.05, which implies that the null hypothesis can be rejected at 5% level of significance. This means that the series under reference are stationary in nature.
Table 7 and 8 presents the results about
the application of Granger’s Causality model to the WPI, CPI and Exchange
Rates of India and Sri Lanka, respectively. The tables test the hypotheses about
the three variables in pairs.
Table 9: 
Vector auto regression for macroeconomic variablesIndia 

The results in case of India show that the probability value for the hypotheses
‘Exchange rate does not Granger Cause LOGCPI’ and ‘LOGCPI does
not Granger Cause LOGEXCHNG’ is more than 0.05 which means that in both
the cases null hypotheses can be accepted. And the same results are observed
in the case of LOGWPI and LOGCPI and LOGWPI and LOGEXCHNG. Similarly, the null
hypothesis can be accepted for all the six cases in the case of Sri Lanka.
Now the Vector Auto Regression (VAR) model is applied on the series under reference in order to further confirm the results produced by the Granger’s Causality model. In Table 9, the study presents the application of Vector Auto Regression (VAR) Model at the three Macroeconomic variables.
By the application of VAR Model for India, the research observes that the integration of macroeconomic variables with the other can be established if the pvalue is more than 1.96. Table 10 shows that the LOGCPI at the lag of 1 and 2, does not have any influence on LOGCPI, LOGWPI and LOGEXCHNG.. Similarly, LOGWPI at a lag of 1 and 2 does not have any influences on the LOGCPI, LOGWPI and LOGXHNG. In LOGXCHNG, the table reveals that LOGXCHNG at a lag of 1 and 2 does not have any effect on the LOGCPI, LOGWPI and LOGXCHNG.
Table 10 shows the application of VAR in case of Macroeconomic variables for Sri Lanka. The table shows that the LOGCPI at the lag of 1 and 2, does not have any influence on LOGWPI and LOGEXCHNG. However, it influences the returns at LOGCPI in period 0. Similarly, LOGEXCHNG at a lag of 1 and 2 does not have any influences on the LOGCPI, LOGWPI and LOGEXHNG. LOGWPI at the lag 1 does not have any influence on LOGCPI and LOGEXCHNG but it influences the return at LOGWPI in period 0. LOGWPI at lag 2 LOGWPI have influence on LOGCPI but it does not influence LOGEXCHNG and LOGWPI.
Table 10: 
Vector auto regression for macroeconomic variables of Sri
Lanka 

Table 11: 
Variance decomposition analysis of India 

Table 12: 
Variance decomposition analysis of Sri Lanka 

Finally, the Variance Decomposition Analysis of the three macro economic variables
is presented in Tables 11 and 12. The
results decompose the values at the three macro economic variables for a period
ranging from 1 to 10. Table 11 implies that on LOGCPI, the
impact of other two macro economic variables is negligible. Rather the LOGCPI
itself with the lag of 1 through 10 impacts the LOGCPI in the current period.
However, the Table 11 reveals that in the case of LOGWPI,
there is visible impact of LOGCPI for periods 1 to 10 and LOGEXCHNG for the
periods 2 to 10. In LOG WPI the impact on LOGCPI is more than the LOGEXCHNG.
In the case of LOGEXCHNG, there is also visible impact of LOGCPI and LOGWPI
for the periods of 2 to 10. The impact is more in the case of LOGCP than the
LOGWPI. Variance Decomposition Analysis shows that the macro economic variables
under study are not much influenced by each other.
The Variance Decomposition Analysis for Sri Lanka is presented in Table 12. It implies that on LOGCPI, the impact of other two macro economic variables is visible. The impact is near about constant in the LOG of Exchange rate but in LOG of WPI impact increases step by step than the previous one. However, the table reveals that in the case of LOGEXCHNG, there is visible impact of LOGWPI for periods 2 to 10 and no impact on LOGCPI. In the case of LOGWPI, there is also visible impact of LOGEXCHNG for the periods of 3 to 10. Variance Decomposition Analysis shows that the macro economic variables under study are not much influenced by each other.
CONCLUSION
The study observes that the development of Indian economy is by far ahead of
that of the Sri Lankan economy. While on one hand the Sri Lankan economy witnesses
a weaker currency and higher interest rates, on the other hand, the Indian economy
demonstrates higher Gross Domestic Product, Gross National Income and Balance
of Payments. The application of Regression analysis shows that the macroeconomic
variables including exchange rates, bank rates, Wholesale Price Index, Consumer
Price Index, Gross National Income and Balance of Payments play a pivotal role
in determining the Gross Domestic Product in India and Sri Lanka. Patra
and Poshakwale (2006), Sharma and Wongbangpo (2002)
and Mukherjee and Naka (1995) also support the fact
that there is a significant impact of the macroeconomic variables on the stock
prices and GDP. The current study further finds that the changes in macroeconomic
variables are higher in case of Sri Lanka than those in India. The standard
deviation, variance and coefficient of variation are also higher in the case
of Sri Lanka than those in India. This signals a higher volatility in the macroeconomic
performance of Sri Lanka than that in India.
The application of econometric tools gives contrasting results so far as the
impact of WPI, CPI and Exchange rate for India and Sri Lanka are concerned.
The application of Unitroot test (Augmented DickeyFuller test) reveals the
series of WPI, CPI and Exchange rates of India and Sri Lanka to be stationary
in nature. Granger’s causality model shows no impact of any variable on
the other in both the countries. Chen et al. (1986),
Liu and Shrestha (2008) and Hasan
and Javed (2009) shows the presence of a longterm relationship between
the equity market and monetary variables, such as, money supply, treasury bill
rates, foreign exchange rates and the consumer piece index in the U.S., China
and Pakistan. The application of the VAR model implies that the in the case
of Sri Lanka, Consumer Price Index at the lag of 1 and 2 influences the CPI
at lag 0. Similarly Wholesale Price Index at the lag 1 influences the WPI at
lag 0. The Variance Decomposition Analysis (for India) implies that the impact
of WPI and Exchange rate on CPI is negligible. Rather the CPI itself with the
lag of 1 through 10 impacts the CPI in the current period. However, in the case
of LOGWPI, there is visible impact of CPI for periods 1 to 10 and Exchange rate
for the periods 2 to 10. In WPI the impact on CPI is more than the Exchange
rate. In the case of Exchange, there is also visible impact of CPI and WPI for
the periods of 2 to 10. The impact is more in the case of CPI than the WPI.
Variance Decomposition Analysis (for Sri Lanka) implies that on CPI, the impact
of other twomacro economic variables is visible. The impact is near about constant
in the case of Exchange rate but in the case of WPI impact increases step by
step than the previous one. However, on the Exchange rate, there is visible
impact of WPI for periods 2 to 10 and no impact of CPI. In the case of WPI,
there is also visible impact of Exchange rate for the periods of 3 to 10.
IMPLICATIONS
The research observes that the Sri Lankan economy is in the dire need of some measures that help the economy to move faster on the development path. Highly volatile economy of Sri Lanka is too risky for the investors to consider investing in Sri Lanka. Further, the research also points to the fact that there is a need for the economy managers of India and Sri Lanka to try improving on different macroeconomic indicators separately since there are few linkages between the performances with regard to these different indicators. The paper will go a long way in assisting the economic policy makers of the countries who may be interested in finding out whether an improvement in one macroeconomic variable will get replicated in the other macroeconomic variable as well. Further, the study will also comment on the present state of the Indian and Sri Lankan economies and which macroeconomic fronts of the two economies call for policymakers attention.
LIMITATION
The present study studied the effect of the macroeconomic variables on the economic output by using the CPI, WPI, GDP and GNI as the variables. But there are certain other factors such as political conditions; global economic environment etc also affects the economic performance.