Impact of macroeconomic variables.docx

7/28/2019 Impact of macroeconomic variables.docx

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Impact of macroeconomic variables on economic indicators:

An Empirical Study of India and Sri Lanka(Gurvinder Singh)

1.IntroductionOver thirty years the relationship between macroeconomic variables and stock market prices has

been an attractive subject for both financial and macro economists. Although there are a number

of studies that investigate the link between macroeconomic variables and stock market, both the

academics and the practitioners have not arrived at a consensus on the direction of the causality

among these variables, which remained as a source of ambiguity.An efficient capital market is

one in which security prices adjust rapidly to the arrival of new information and, therefore, the

current prices of securities reflect all information about the security. What this means, in simple

terms, is that no investor should be able to employ readily available information in order to

predict stock price movements quickly enough so as to make a profit through trading shares.

For the past three decades evidence that key macroeconomic variables help predict the time

series of stock returns has accumulated in direct contradiction to the conclusions drawn by the

EMH. The onslaught against the conclusions drawn from the EMH includes early studies by

Fama and Schwert (1977) and Jaffe and Mandelker (1976), all affirming that macroeconomic

variables influence stock returns. Again Chen, Roll and Ross (1986), having first illustrated that

economic forces affect discount rates, the ability of firms to generate cash flows, and future

dividend payouts, provided the basis for the belief that a long-term equilibrium existed between

stock prices and macroeconomic variables. More recently, Granger (1986) proposed to

determine the existence of long-term equilibrium among selected variables through cointegration

analysis, paving the way for a (by now) preferred approach to examining the economic variables-

stock markets relationship. A set of time-series variables are cointegrated if they are integrated of

the same order and a linear combination of them is stationary. Such linear combinations would

then point to the existence of a long-term relationship between the variables. An advantage of

cointegration analysis is that through building an error-correction model (ECM), the dynamic co-

movement among variables and the adjustment process toward long-term equilibrium can be

examined,Maysami et al (2004).

More recently, Granger (1986) and Johansen and Juselius (1990) proposed to determine the

existence of long-term equilibrium among selected variables through cointegration analysis,

paving the way for a (by now) preferred approach to examining the economic variables-stock

markets relationship. A set of time-series variables are cointegrated if they are integrated of the

same order and a linear combination of them is stationary. Such linear combinations would then

point to the existence of a long-term relationship between the variables. An advantage of

cointegration analysis is that through building an error-correction model (ECM), the dynamic co-

movement among variables and the adjustment process toward long-term equilibrium can be

examined.


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Despite the importance of previous studies, until now the majority of research considers

developed countries financial markets, which are efficient enough and do not suffer from the

inefficiency problems in less developed countries. Considering this matter, the subject of

financial markets in developing countries still needs lengthy analysis and more research

attention.

2.Objectives

The study aims to achieve the following objectives:

1. To study the pattern of CPI, WPI, GDP, GNI and Rate of interest in India and Sri Lanka

for the year 2002-2009

2. To study the impact of macroeconomic variable on GDP growth of the Indian and Sri

Lankan economy.

3. To comparatively analyze the impact of macro-economic variable on GDP growth in

India viz-a-viz Sri Lanka

3.Literature ReviewFor number of years, there has been an extensive debate in the literature assessing the influence

of macroeconomics variables on the stock return. The economic theory, in explaining this

interrelationship, suggests that stock prices should reflect expectations about futures corporate

performance. Corporate profits on the other hand generally may reflect the level of countrys

economic activities. Thus, if stock prices accurately reflect the underlying fundamentals, then the

stock prices should be employed as leading indicators of future economic activity. However, if

economic activities reflect the movement of stock prices, the results then should be the opposite,

i.e economic activities should lead stock price. Therefore, the causal relations and dynamics

interactions among economics factors and stock prices are important in the formulation of

nations macroeconomic policy. According to Oberuc (2004), the economic factors which,

usually associated with stock prices movement and being considered greatly by researchers are

dividend yield, industrial production, interest rate, term spread, default spread, inflation,

exchange rates, money supply, GNP or GDP and previous stock returns, among others.

Emerging stock markets have been identified as being at least partially segmented from global

capital markets. In direct contradiction to the conclusions drawn by the EMH, evidence that key

macroeconomic variables help predict the time series of stock returns has accumulated for nearly

30 years.

Numerous studies have investigates the relationship between stock returns, interest rates,

inflation and real activity (see, inter alia, Fama (1981, 1990), James et al. (1985), Fama and

Schwert (1977), Geske and Roll (1983), Mandelker and Tandon (1985), Hendrys (1986), Chen,

Roll and Ross(1986), Darrat and Mukherjee (1987), Fama and French (1989) , Asprem

(1989),Martinez & Rubio, 1989, Schwert, 1989,Schwert (1990), Ferson and Harvey (1991), Lee

(1992), Mukherjee and Naka (1995), Chatrath et al. (1997), Naka A., Mukherjee T. and Tufte D.


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(1998), Ibrahim (1999) , Gjerde & Saettem, 1999, Wongbangpo and Sharma (2002), , and

Vuyyuri (2005).[

Now we divide the whole work of the researchers on macroeconomic variables in different parts

on the basis of stock markets, tests and conclusions.

Fama (1981) focuses upon the correlation between stock returns and expected and unexpectedinflation in the U.S., showing that the observed negative relation is a proxy effect for more

fundamental relationships between stock returns and real activity. James et al. (1985) finds

strong links between stock returns, real activity and money. Investigating the stock return-

inflation relation for the U.S., U.K., Canada and German.Fama and Schwert (1977) estimate the

extent to which various assets were hedges against the expected and unexpected components of

the inflation rate during the 19531971 period. They finds that U.S. government bonds and bills

were a complete hedge against expected inflation, and private residential real estate was a

complete hedge against both expected and unexpected inflation. Geske and Roll (1983) offers a

supplementary explanation suggesting that stock prices signal changes in expected inflation

because money supply responds to changes in expected real activity. Mandelker and Tandon(1985) tests whether the negative relationship between real stock returns and inflation in the

United States is in fact proxying for a positive relationship between stock returns and real

activity variables in six major industrial countries over 19661979. Hendrys (1986) approach

which allows making inferences to the short-run relationship between macroeconomic variables

as well as the long-run adjustment to equilibrium, they analysed the influence of interest rate,

inflation, money supply, exchange rate and real activity, along with a dummy variable to capture

the impact of the 1997 Asian financial crisis. Chen, Roll and Ross (1986), having first illustrated

that economic forces affect discount rates, the ability of firms to generate cash flows, and future

dividend payouts, provided the basis for the belief that a long-term equilibrium existed between

stock prices and macroeconomic variables. Darrat and Mukherjee (1987) finds a significant

causal (lagged) relationship between stock returns and some selected macro variables, including

money supply, implying market inefficiency in the semi-strong sense on the Indian data over

19481984. Fama and French (1989) finds that expected returns on common stocks and long-

term bonds contain a term or maturity premium that has a clear business-cycle pattern (low near

peaks, high near troughs). Expected returns also contain a risk premium that is related to longer-

term aspects of business conditions. The variation through time in this premium is stronger for

low-grade bonds than for high-grade bonds and stronger for stocks than for bonds. The general

message is that expected returns are lower when economic conditions are strong and higher when

conditions are weak. Asprem (1989) investigates the relationship between stock indices, assetportfolios and macroeconomic variables in ten European countries. It is shown that employment,

imports, inflation and interest rates are inversely related to stock prices. Expectations about

future real activity, measures for money and the U.S. yield curve are positively related to stock

prices. Schwert, 1989 in analyzes the relation of stock volatility with real and nominal

macroeconomic volatility, economic activity, financial leverage, and stock trading activity using

monthly data from 1857 to 1987. Ferson and Harvey (1991) provides an analysis of the


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predictable components of monthly common stock and bond portfolio return. Most of the

predictability is associated with sensitivity to economic variables in a rational asset pricing

model with multiple betas. The stock market risk premium is the most important for capturing

predictable variation of the stock portfolios, while premiums associated with interest rate risks

capture predictability of the bond returns. Time variation in the premium for beta risk is more

important than changes in the betas. Lee (1992) investigates causal relations and dynamic

interactions among asset returns, real activity, and inflation in the postwar United States. Major

findings are (1) stock returns appear Granger-causally prior and help explain real activity, (2)

with interest rates in the VAR, stock returns explain little variation in inflation, although interest

rates explain a substantial fraction of the variation in inflation, and (3) inflation explains little

variation in real activity. Mukherjee and Naka (1995) investigates whether cointegration exists

between the Tokyo Stock Exchange index and six Japanese macroeconomic variables, namely

the exchange rate, money supply, inflation, industrial production, long-term government bond

rate, and call money rate. They find that a cointegrating relation indeed exists and that stock

prices contribute to this relation. Chatrath et al. (1997) investigates a negative relationshipbetween stock market returns and inflationary trends has been widely documented for developed

economies in Europe and North America. This study provides similar evidence for India. Naka

A., Mukherjee T. and Tufte D. (1998) analyze relationships among selected macroeconomic

variables and the Indian stock market. They find that three long-term equilibrium relationships

exist among these variables. These results suggest that domestic inflation is the most severe

deterrent to Indian stock market performance, and domestic output growth is its predominant

driving force. After accounting for macroeconomic factors, the Indian market still appears to be

drawn downward by a residual negative trend. Ibrahim (1999) investigates the dynamic

interactions between seven macroeconomic variables and the stock prices for an emerging

market, Malaysia. The results strongly suggest informational inefficiency in the Malaysianmarket. The bivariate analysis suggests cointegration between the stock prices and three

macroeconomic variables - consumer prices, credit aggregates and official reserves. From

bivariate error-correction models, we note the reactions of the stock prices to deviations from the

long run equilibrium. Gjerde & Saettem, 1999 investigates to what extent important results on

relations among stock returns and macroeconomic factors from major markets are valid in a

small, open economy. Wongbangpo and Sharma (2002) investigates the role of select

macroeconomic variables, i.e., GNP, the consumer price index, the money supply, the interest

rate, and the exchange rate on the stock prices in five ASEAN countries (Indonesia, Malaysia,

Philippines, Singapore, and Thailand). They observe long and short term relationships between

stock prices and these macroeconomic variables. Vuyyuri (2005) investigates the cointegration

relationship and the causality between the financial and the real sectors of the Indian economy

using monthly observations from 1992 through December 2002.

Different methods of data analysis have been put into use by the researchers in their studies

about the effect of macroeconomic variables on economy in the case of India, Sri Lanka, U.S.,

U.K., Canada, Germany, Netherland, Switzerland, European countries and ASEAN countries


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with each other or with country(s) from the other parts of the world. Grangers Causality Model,

Cointegration techniques (particularly Johansens Model), VECM and Vector Auto Regression

Model are the prominent ones that have been used to analyze the data about the effect of

macroeconomic variables on economy. However, a number of researches have used only one or

at the most two methods to analyze the data.

Grangers causality model has also been used very extensively by the researchers. Darrat and

Mukherjee (1987) , Wongbangpo and Sharma (2002), Ibrahim (1999), Vuyyuri (2005) applying

Granger-type causality. Ibrahim (1999), Wongbangpo and Sharma (2002) Cheung and Ng

(1998), Vuyyuri (2005), Johansen, S. & Juselius, K. 1990 using the Johansen cointegration

technique using cointegration.Ferson and Harvey (1991) using rational asset pricing model with

multiple betas. James et al. (1985) ,Lee (1992), Gjerde & Saettem, 1999 Using a multivariate

vector autoregression (VAR) approach, uses a VARMA approach. Mukherjee and Naka (1995),

Naka A., Mukherjee T. and Tufte D. (1998) use Johansen's (1991) vector error correction model

(VECM) .Chatrath et al. (1997) using heteroscedasticity and autocorrelation corrected models.

The current study contributes to the literature in numerous ways. First, this is the study

concentrating on the economy India and Sri Lanka; and studies the linkages within these rather

than with the developed world. Secondly, it uses a combination of the various methods used

empirically to analyze the data.

4.Research Methodology

In this study 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

on-line data source. Index Numbers (2000=100) is used as the base index for the whole research

data. We filled the missing values by taking the average of two of the preceding cases and two of

the succeeding cases.

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, we prepare the line graph for each of the series. Further, we perform the Augmented

Dickey-Fuller test under the unit root test to finally confirm whether or not the series are

stationary. For the basic understanding of Unit root testing, we may look at the following

equationyt = yt1 + xt+ t , (1.1)

where xt are optional exogenous regressors which may consist of constant, or a constant and

trend, and are parameters to be estimated, and the t are assumed to be white noise. If || 1 ,y is a nonstationary series and the variance of y increases with time and approaches infinity. If

||< 1 , y is a (trend-)stationary series. Thus, we evaluate the hypothesis of (trend-) stationarityby testing whether the absolute value of || is strictly less than one.


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The Standard Dickey-Fuller test is carried out by estimating equation (1.2) after subtracting y t-1

from both sides of the equation.

yt = y t-1 + xt+ t, (1.2)

where = - 1. The null and alternative hypotheses may be written as,

H0 : = 0H1: < 0 (1.3)

In order to make the series stationary, we take the log of the three series and arrive at the daily

return of the three series. All the remaining analysis is performed at the daily return (log of the

series) of WPI, CPI and Exchange rates.

At the stationary log series of the three stock exchanges, we perform the Grangers causality

model in order to observe (i) whether the LOG of WPI granger causes the LOG of Exchange rate

and/or at CPI; (ii) whether the LOG of Exchange rate granger causes the return at LOG of CPI

and/or at LOG of WPI; and (iii) whether the LOG of CPI granger causes the LOG of WPI and/or

at Exchange Rate.

The Granger (1969) 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 Granger-caused by x if x helps in the prediction of y

, or equivalently if the coefficients on the lagged x s are statistically significant. I t is pertinent to

note that two-way 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 Grangers Causality, there are

bivariate regressions of the under-mentioned form

yt = 0 + 1 yt-1 + + lyt-l+ 1 xt-1 + + lxt-l+ t

xt = 0 + 1 xt-1 + + lxt-l+ 1 yt-1 + + lyt-l+ t (1.4)

for all possible pairs of (x, y) series in the group. In equation (1.4), we take lags ranging from 1

to l. In Grangers model, one canpick a lag length, lthat corresponds to reasonable beliefs about

the longest time over which one of the variables could help predict the other. The reported F-

statistics are the Wald statistics for the joint hypothesis:

1 = 2 = = t = 0 (1.5)

for each equation. The null hypothesis is that x does not Granger-cause y in the first regression

and that y does not Granger-cause x in the second regression.

We follow the application of Grangers causality with the Vector Auto Regression (VAR)

Model. The Vector Auto Regression (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


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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:

yt = A1 y t-1 + + Ap y t-p + Bxt + t (1.6)

where yt is a k vector of endogenous variables, xt is a d vector of exogenous variables, A1,

, Ap and B are matrices of coefficients to be estimated, and t is a vector of innovationsthat may be contemporaneously correlated but are uncorrelated with their own lagged values and

uncorrelated with all of the right-hand side variables.

Finally, we apply the Variance Decomposition Analysis in order to finally quantify the extent

upto which the three 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.

5.Results of the Study5.1 Descriptive Statistics and Correlation matrix of Indian yearly data

Table 5.1

Mean Median Min. Max. Variance Std.Dev. Coef.

Var.

Skewness Kurtosis

Exchange

Rates

45.40 45.31 41.35 48.61 6 2.46 5.4240 -0.168645 -0.47373

Bank

Rates

6.03 6.00 6.00 6.25 0 0.09 1.4655 2.828427 8.00000

WPI

130.63 129.40 107.50 154.55 289 17.01 13.021

0

0.176321 -1.19579

CPI

129.58 125.05 108.20 164.36 371 19.26 14.859

8

0.835715 -0.12527

GDP

40485.45 38579.55 24545.60 61641.80 172467608 13132.69 32.438

1

0.449978 -1.00381

GNI

40437.96 38300.15 24378.70 61386.40 178127021 13346.42 33.004

7

0.452074 -1.11894

Balance

of

Payments

-1383.60 -1155.10 -3439.50 -244.20 1233016 1110.41 -

80.255

3

-0.952124 0.17422

From the above table in which descriptive values of all the variables have been calculated shows

that standard deviation is very high in case of GNI comparative to others which portrays nothing

but that it is dispersed around its mean value by 13346.42 i.e., there is high volatility in its

values. From the skewness measure we found that exchange rates and balance of payment is

negatively skewed while bank rates are more positively skewed compared to other variables. In


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case of kurtosis, all variables are negatively skewed except bank rates and balance of payments.

Next step is to check out the correlation between the variables in consideration in this study.

.Table 5.2

Exchange

Rates

Bank

Rates

WPI CPI GDP GNI Balance of

Payments

Exchange Rates 1.00 0.53 -0.30 -0.11 -0.22 -0.23 0.33

Bank Rates 0.53 1.00 -0.55 -0.45 -0.49 -0.49 0.41

WPI -0.30 -0.55 1.00 0.97 0.99 0.99 -0.94

CPI -0.11 -0.45 0.97 1.00 0.99 0.99 -0.89

GDP -0.22 -0.49 0.99 0.99 1.00 1.00 -0.92

GNI -0.23 -0.49 0.99 0.99 1.00 1.00 -0.93

Balance of Payments 0.33 0.41 -0.94 -0.89 -0.92 -0.93 1.00

In the table 5.2 there is a positive correlation between Exchange rates - Bank rates, Exchangerates-Balance of payments, Bank rates - Balance of payments, W.P.I. - C.P.I., WPI - G.D.P.,

WPI-GNI, CPI - GDP and CPI-GNI.

In the same table there is a negative correlation between Exchange rates - WPI, Exchange rates -

CPI, Exchange rates-GDP, Exchange rates GNI, Bank Rates -WPI, Bank Rates -CPI, Bank

Rates GDP, Bank Rates GNI, WPI - Balance of Payments, CPI - Balance of Payments.

Balance of payments - GDP and Balance of payments - GNI. In table highlighted values are

significant at 0.05 level of significance.

When GDP is Dependent Variable

Table 5.3

Coefficientsa

Model Unstandardized Coefficients Standardized

Coefficients

t Sig.

B Std. Error Beta

1 (Constant) -5371.526 22912.473 -.234 .853

Exchange Rates -7.066 82.443 -.001 -.086 .946

Bank Rates 568.306 1987.636 .004 .286 .823

WPI 23.592 105.386 .031 .224 .860

CPI -16.876 102.537 -.025 -.165 .896

GNI 1.079 .254 1.097 4.244 .147

Balance of Payments 1.298 .263 .110 4.928 .127

a. Dependent Variable: GDP

From table 5.3, we can formulate 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,


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CPI, GNI and Balance of Payments). Hence, we arrive 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. Using this regression equation to the entire series

we find out the predicted values of GDP for the given values of independent variables. These

values shall be presented later in this section.

Table 5.4

ANOVA

Model Sum of

Squares

df Mean Square F Sig.

1 Regression 1.207E9 6 2.012E8 5542.540 .010a

Residual 36302.156 1 36302.156

Total 1.207E9 7

a. Predictors: (Constant), Balance of Payments, Exchange rates, Bank Rates, CPI, WPI, GNI

b. Dependent Variable: GDP

Table 5.4 shows the anova table in which we find 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 we can look at is the level of significance the value of which is found to be 0.010. This value

is significant at 5% level of significance. Looking at the sum of squares, we find that the

regression equation accounts for a major proportion of the values of the dependent variable

(GDP). The detailed values of GDP at every level of independent variables are presented in the

table 5.5 below.

Table 5.5

Predicted & Residual Values

Observed - Value

Predicted - Value

Residual

Standard - Pred.

v.

Standard-

Residual

Std.Err. -Pred.Val

Mahalanobis -

Distance

Deleted -Residual

Cook's -Distance

2002 24545.60 24545.60 0.000 -1.21377 0.000000 190.5313 6.125000

2003 27546.20 27534.31 11.887 -0.98619 0.062387 190.1600 6.097747 3053.2 36.5404

2004 31494.10 31614.09 -119.986 -0.67553 -0.629746148.0050 3.348946 -302.6 0.2174

2005 35867.40 35723.53 143.867 -0.36261 0.755085 124.9178 2.133947 252.3 0.1077

2006 41291.70 41294.22 -2.523 0.06159 -0.013244190.5145 6.123771 -14374.9 813.1068

2007 47234.00 47256.65 -22.652 0.51561 -0.118890189.1799 6.026056 -1602.6 9.9639

2008 54262.80 54283.76 -20.961 1.05070 -0.110013189.3746 6.040268 -1731.7 11.6576

2009 61641.80 61631.43 10.371 1.61020 0.054433 190.2488 6.104265 3501.2 48.0961

Min. 24545.60 24545.60 -119.986 -1.21377 -0.629746124.9178 2.133947 -14374.9 0.1077

Max. 61641.80 61631.43 143.867 1.61020 0.755085 190.5313 6.125000 3501.2 813.1068

Mean 40485.45 40485.45 0.000 0.00000 0.000001 176.6165 5.250000 -1600.7 131.3843

Median 38579.55 38508.88 -1.262 -0.15051 -0.006622189.7673 6.069008 -302.6 11.6576


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From table 5.5, we find that the predicted values in most of the cases are quite near to the 1% of

the observed values except one case of year 2004, which indicates that there is a significant

impact of the independent variables on the GDP. Besides presenting the predicted values of the

series, table 5.5 also presents the residual value, standardized predicted value, standard error of

the predicted value, Mahalanobis distance, deleted residual, and Cooks distance.

When GNI is Dependent Variable

Table 5.6

Coefficientsa

Model Unstandardized Coefficients Standardized Coefficients t Sig.

B Std. Error Beta

1 (Constant) 526.488 21214.056 .025 .984

Exchange Rates -5.809 74.386 -.001 -.078 .950

Bank Rates -198.429 1853.503 -.001 -.107 .932

WPI -1.903 97.361 -.002 -.020 .988CPI 34.711 87.036 .050 .399 .758

Balance of Payments -1.149 .325 -.096 -

3.540

.175

GDP .878 .207 .864 4.244 .147

a. Dependent Variable: GNI


dependent variable (GNI) and X is the independent variable (Exchange rates, Bank rates, WPI,

CPI, GDP and Balance of Payments). Hence, we arrive at the regression equation GDP =

526.488 + (-5.809) Exchange Rates + (-198.429) Bank Rates + (-1.903) WPI + (34.711) CPI +

(-1.149) Balance of Payments + (.878) GDP. Using this regression equation to the entire serieswe find out the predicted values of GDP for the given values of independent variables. These

values shall be presented later in this section in the table 5.7

Table 5.7

ANOVA

Model Sum of Squares df Mean Square F Sig.

1 Regression 1.247E9 6 2.078E8 7040.431 .009a

Residual 29516.652 1 29516.652

Total 1.247E9 7

a. Predictors: (Constant), GDP, Exchange Rates, Bank Rates, Balance of Payments, WPI, CPIb. Dependent Variable: GNI

Table 5.7 shows the anova table in which we find 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 we can look at is the level of significance the value of which is found to be 0.009. This value

is significant at 5% level of significance. Looking at the sum of squares, we find that the


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(GNI). The detailed values of GNI at every level of independent variables are presented in the

table 5.8 below.

Table 5.8


Observed

- Value

Predicted

- ValueResidual

Standard -

Pred. v.

Standard -

Residual

Std.Err. -

Pred.Val

Mahalano

bis -

Distance

Deleted -

Residual

Cook's -

Distance

2002 24378.70 24378.70 0.000 -1.20328 0.000000 171.8041 6.125000

2003 27339.10 27336.82 2.279 -0.98163 0.013267 171.7890 6.123767 12938.99 810.3730

2004 31270.30 31185.56 84.742 -0.69326 0.493249 149.4505 4.421945 348.31 0.4443

2005 35606.30 35749.23 -142.926 -0.35131 -0.831911 95.3332 1.280356 -206.51 0.0636

2006 40994.00 40966.63 27.367 0.03961 0.159293 169.6106 5.947396 1078.64 5.4881

2007 46995.60 46970.22 25.379 0.48944 0.147720 169.9197 5.972288 1163.32 6.40702008 55533.30 55515.84 17.461 1.12975 0.101633 170.9147 6.052714 1690.88 13.6948

2009 61386.40 61400.70 -14.297 1.57068 -0.083216 171.2083 6.076534 -2064.93 20.4940

Min. 24378.70 24378.70 -142.926 -1.20328 -0.831911 95.3332 1.280356 -2064.93 0.0636

Max. 61386.40 61400.70 84.742 1.57068 0.493249 171.8041 6.125000 12938.99 810.3730

Mean 40437.96 40437.96 0.001 -0.00000 0.000004 158.7538 5.250000 2135.53 122.4235

Medi

an38300.15 38357.93 9.870 -0.15585 0.057450 170.4172 6.012501 1078.64 6.4070

From table 5.8, we find that the predicted values in all cases are quite near to the 1% of the

observed values from year 2002 to 2009, which indicates that there is a significant impact of the

independent variables on the GNI. Besides presenting the predicted values of the series, table 5.8

also presents the residual value, standardized predicted value, standard error of the predicted

value, Mahalanobis distance, deleted residual, and Cooks distance.

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, we take log of the

three series on whichthe further analysis shall be performed. In this way, three new variables are

created and we assign those, names LOGExchange, LOGWPI and LOGCPI which denote the

LOG of Exchange rate, WPI and CPIrespectively. Going further in the paper, we shall discuss

the linkages between the logs of exchange rate, WPI and CPI

Table 5.9 presents the descriptive statistics of the series of LOGExchange, LOGWPI and

LOGCPI


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12

Table 5.9

Descriptive statistics of the Exchange rates, WPI and CPI

Table 5.9 shows that the mean at the Exchange rates, WPI and CPI happens to be 45.4384,

130.7451 and 133.9192 respectively. Since there are a total of 96 observations for a period of 8

years. The value of median is highest in the case of WPI than the CPI and Exchange rates which

are 128.9000, 127.5890 and 45.5355 respectively. The variance and standard deviation in the

case of CPI is higher than the WPI and Exchange rate which shows that the volatility is more in

CPI than the others. In Figure 1 to 4 present the line graphs of the LOG of WPI, LOG of CPI andLOG of Exchange rate of India. While the return on WPI, CPI and Exchange Rates are

individually presented in figures 1 to 3, figure 4 presents common line graphs for the three macro

economic variables under study.

5.2 Stationarity and Causality Analysis of Indian Monthly data

After all these statistics stationarity tests are carried out on the variables because to apply

Granger causality, 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.

LOGExchange LOGWPI LOGCPI

Valid N 96 96 96

Mean 45.4384 130.7451 133.9192

Median 45.5355 128.9000 127.5890

Frequency 1 2 5

Minimum 39.3740 104.7800 113.8000

Maximum 51.2290 161.8180 184.6630

Variance 7.5766 266.5260 311.2582

Std.Dev. 2.75256 16.32562 17.64251

Coef.Var. 6.05778 12.48660 13.17400Skewness -0.456286 0.217605 1.302944

Kurtosis -0.29634 -1.07108 0.94365


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13

Figure 1

-.04

-.03

-.02

-.01

.00

.01

.02

.03

.04

.05

10 20 30 40 50 60 70 80 90

LOGWPI

Figure 2

.16

.12

.08

.04

.00

.04

.08

10 20 30 40 50 60 70 80 90

LOGCPI


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14

Figure 3

-.12

-.08

-.04

.00

.04

.08

.12

10 20 30 40 50 60 70 80 90

LOGXCHNG

Figure 4

-.16

-.12

-.08

-.04

.00

.04

.08

.12

10 20 30 40 50 60 70 80 90

LOGCPI LOGW PI LOGXCHNG Figures 1 to 4 demonstrate the value of the three macro economic variables. It is indicated from

the figures that values at all the three macro economic variables are stationary in nature. In order

to further check the stationarity of the three series, we perform the Unit Root Test in order to

further confirm the same.


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15

The unit-root 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 unit-root test and the augmented Dickey- Fuller test are

shown below in the following tables.

Table 5.10

Unit root test on LOG exchange

Variable Coefficient Std. Error t-Statistic Prob.

LOGXCHNG_NA(-1) -0.991078 0.104187 -9.512455 0.0000

C -0.000457 0.002292 -0.199305 0.8425

R-squared 0.495854 Mean dependent var -6.41E-05

Adjusted R-squared 0.490374 S.D. dependent var 0.031124

S.E. of regression 0.022219 Akaike info criterion -4.754684

Sum squared resid 0.045419 Schwarz criterion -4.700571

Log likelihood 225.4701 Hannan-Quinn criter. -4.732826

F-statistic 90.48679 Durbin-Watson stat 1.999856

Prob(F-statistic) 0.000000

By the way of unit-root test, the null hypothesis that series Log of Exchange Rates has a unit-root

is tested. Probability value of less than 0.05 in above table shows that the Null hypothesis is

rejected and the variable does not have a unit-root, which confirms that the series is stationary.

Hence, the econometric models can now be applied on the series.

Table 5.11Unit root test on LOG CPI

Variable Coefficient Std.

Error

t-Statistic Prob.

LOGCPI_NA(-1) -0.954503 0.104062 -9.172398 0.0000

C 0.004433 0.001739 2.549087 0.0125

R-squared 0.477667 Mean dependent var 8.68E-05







The probability value of unit-root test in table 5.11 points towards the fact that the null

hypothesis can be rejected at 0.05 level of significance. It implies that the LOG of CPI of India is

also a stationary one. Hence, the econometric models can now be applied on the series.


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Table 5.12

Unit root test on LOG WPI


LOGWPI_NA(-1) -0.931885 0.104279 -8.936453 0.0000C 0.004203 0.001063 3.954774 0.0002

R-squared 0.464681 Mean dependent var -0.000105

Adjusted R-

squared

0.458862 S.D. dependent var 0.012484







hypothesis can be rejected at 0.05 level of significance. It implies that the LOG of WPI of India

is also a stationary one.

Table 5.13

Granger Causality test on India monthly data

Pairwise Granger Causality Tests

Date: 03/11/11 Time: 16:09

Sample: 1 95

Lags: 2

Null Hypothesis: Obs F-Statistic Prob.

LOGXCHNG_NA does not Granger Cause LOGCPI_NA 93 0.01602 0.9841

LOGCPI_NA does not Granger Cause LOGXCHNG_NA 1.42415 0.2462

LOGWPI_NA does not Granger Cause LOGCPI_NA 93 0.19801 0.8207

LOGCPI_NA does not Granger Cause LOGWPI_NA 0.53034 0.5903

LOGWPI_NA does not Granger Cause LOGXCHNG_NA 93 0.66470 0.5170

LOGXCHNG_NA does not Granger Cause LOGWPI_NA 1.11568 0.3323

Table 5.13 presents the results about the application of Grangers Causality model to the WPI,

CPI and Exchange Rates of India. Null hypothesis in the case of Grangers causality model is

that A does not granger cause B. On those lines, table 6 tests the hypotheses about the three

variables in pairs. The results 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 & LOGCPI and LOGWPI & LOGEXCHNG.


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Now we apply the Vector Auto Regression (VAR) model on the series under reference in order

to furtherconfirm the results produced by the Grangers Causality model.

In table 5.14, we present the application of Vector Auto Regression (VAR) Model at the three

stock exchanges.

Table 5.14

Vector Auto Regression test on Indias monthly data

LOGCPI_NA LOGWPI_NA LOGXCHNG_NA

LOGCPI_NA(-1) 0.037950 -0.049043 0.129684

(0.10903) (0.05984) (0.14649)

[ 0.34807] [-0.81950] [ 0.88525]

LOGCPI_NA(-2) -0.007443 -0.032575 0.179728

(0.11010) (0.06043) (0.14793)

[-0.06760] [-0.53905] [ 1.21497]

LOGWPI_NA(-1) 0.058476 0.059458 -0.100699

(0.19499) (0.10703) (0.26199)

[ 0.29989] [ 0.55554] [-0.38435]

LOGWPI_NA(-2) 0.098758 0.201064 0.237570

(0.19686) (0.10805) (0.26450)

[ 0.50167] [ 1.86082] [ 0.89818]

LOGXCHNG_NA(-1) 0.006020 -0.000821 0.000317

(0.07947) (0.04362) (0.10678)

[ 0.07576] [-0.01883] [ 0.00296]

LOGXCHNG_NA(-2) 0.008605 -0.063557 -0.042913

(0.07894) (0.04333) (0.10606)

[ 0.10901] [-1.46688] [-0.40460]C 0.003794 0.003678 -0.002482

(0.00217) (0.00119) (0.00292)

[ 1.74748] [ 3.08648] [-0.85084]

R-squared 0.006754 0.068391 0.042170

Adj. R-squared -0.062543 0.003395 -0.024656

Sum sq. resids 0.024099 0.007260 0.043505

S.E. equation 0.016740 0.009188 0.022492

F-statistic 0.097459 1.052240 0.631044

Log likelihood 252.0453 307.8332 224.5760

Akaike AIC -5.269790 -6.469531 -4.679054

Schwarz SC -5.079165 -6.278906 -4.488429Mean dependent 0.004641 0.004503 -0.000476

S.D. dependent 0.016239 0.009204 0.022219

Determinant resid covariance (dof adj.) 1.16E-11

Determinant resid covariance 9.16E-12

Log likelihood 785.9715

Akaike information criterion -16.45100

Schwarz criterion -15.87912


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By the application of VAR Model, we observe that the integration of macroeconomic variables

with the other can be established if the p-value is more than 1.96. Table 5.14 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 5.15

Variance Decomposition on Indias monthly data

Variance Decomposition of LOGCPI_NA:

Period S.E. LOGCPI_NA LOGWPI_NA LOGXCHNG_NA

1 0.016740 100.0000 0.000000 0.000000

2 0.016764 99.88856 0.104968 0.006467

3 0.016793 99.54793 0.431889 0.020176

4 0.016795 99.53270 0.444589 0.022712

5 0.016797 99.50979 0.458547 0.031663

6 0.016797 99.50889 0.459146 0.031967

7 0.016797 99.50841 0.459344 0.032243

8 0.016797 99.50839 0.459348 0.032259

9 0.016797 99.50839 0.459348 0.032264

10 0.016797 99.50839 0.459348 0.032264

Variance Decomposition of LOGWPI_NA:


1 0.009188 2.360903 97.63910 0.000000

2 0.009233 2.975686 97.02392 0.000397

3 0.009505 2.963575 94.76938 2.267042

4 0.009513 3.076295 94.64900 2.274704

5 0.009523 3.178698 94.48968 2.331623

6 0.009523 3.184630 94.48252 2.3328557 0.009523 3.187336 94.47914 2.333527

8 0.009523 3.187458 94.47900 2.333538

9 0.009523 3.187478 94.47899 2.333537

10 0.009523 3.187478 94.47899 2.333537

Variance Decomposition of

LOGXCHNG_NA:


1 0.022492 0.051339 0.818124 99.13054

2 0.022602 0.856645 0.973590 98.16977

3 0.022984 3.134815 1.764871 95.10031

4 0.022986 3.135866 1.773601 95.09053

5 0.022993 3.142175 1.818193 95.03963

6 0.022994 3.142716 1.819371 95.03791

7 0.022994 3.143564 1.820026 95.03641

8 0.022994 3.143676 1.820051 95.03627

9 0.022994 3.143726 1.820051 95.03622

10 0.022994 3.143729 1.820050 95.03622

Cholesky Ordering: LOGCPI_NA

LOGWPI_NA LOGXCHNG_NA


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Finally, the Variance Decomposition Analysis of the three macro economic variables is

presented in the table 5.15. The table decomposes the values at the three macro economic

variables for a period ranging from 1 to 10.

The Variance Decomposition Analysis as presented in table 5.15. It 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 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.

5.3Descriptive Statistics and Correlation matrix of Sri Lanka yearly data

Table 5.16

Mean Median Minimu

m

Maximu

m

Variance Std.

Dev.

Coef.

Var.

Skewnes

s

Kurtosis

Exchange

Rates

104 103 96 115 4.685914E+01 7 6.5846 0.39081 -1.01195

Bank Rates 15 15 15 18 1.125000E+00 1 6.8986 2.82843 8.00000

WPI

187 169 124 277 3.691617E+03 61 32.449

9

0.57728 -1.41353

CPI

181 168 125 258 2.644244E+03 51 28.416

3

0.62302 -1.21344

GDP29694

08269573

11636037 4825085 1.432158E+12 119672

840.301

90.54621 -1.24436

GNI

29279

48

266049

5

1611994 4769271 1.377908E+12 117384

3

40.091

0

0.54585 -1.21101

Balance of

Payments

-

27901

7

-266482 -603649 -106481 2.685934E+10 163888 -

58.737

8

-1.04250 1.26959

From the above table in which descriptive values of all the variables have been calculated shows

that standard deviation is very high in case of Balance of Payments comparative to others which

portrays nothing but that it is dispersed around its mean value by 163888 i.e., there is high

volatility in its values. From the skewness measure we found that only balance of payment isnegatively skewed while bank rates are more positively skewed compared to other variables. In

case of kurtosis, all variables are negatively skewed except bank rates and balance of payments.

Next step is to check out the correlation between the variables in consideration in this study.


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20

Table 5.17

Correlations

Exchange

Rates

Bank Rates WPI CPI GDP GNI Balance of

Payments

Exchange Rates 1.00 -0.49 0.92 0.93 0.95 0.95 -0.71Bank Rates -0.49 1.00 -0.42 -0.44 -0.45 -0.45 0.43

WPI 0.92 -0.42 1.00 0.99 0.99 0.99 -0.88

CPI 0.93 -0.44 0.99 1.00 1.00 1.00 -0.84

GDP 0.95 -0.45 0.99 1.00 1.00 1.00 -0.82

GNI 0.95 -0.45 0.99 1.00 1.00 1.00 -0.81

Balance of Payments -0.71 0.43 -0.88 -0.84 -0.82 -0.81 1.00

In the table 5.17 there is a positive correlation between Exchange rates -WPI, Exchange rates-

CPI, Exchange rates-GDP, Exchange rates- GNI, Bank rates - Balance of payments, W.P.I. -

C.P.I., WPI - G.D.P. and WPI-GNI

In the same table there is a negative correlation between Exchange ratesBalance of Payments,

Exchange rates Bank Rates, Bank Rates -WPI, Bank Rates -CPI, Bank Rates GDP, Bank

Rates GNI, WPI - Balance of Payments, CPI - Balance of Payments, GDP-Balance of

payments and GNI-Balance of payments. In table highlighted values are significant at 0.05 level

of significance.

GDP as a Dependent variable

Table 5.18

Coefficientsa


Coefficients

t Sig.

B Std. Error Beta

1 (Constant) -199957.986 227360.801 -.879 .541

xchnge -203.156 1830.257 -.001 -.111 .930

Bank Rates 4508.881 1747.712 .004 2.580 .235

WPI -854.678 599.947 -.043 -1.425 .390

CPI 3164.948 1370.841 .136 2.309 .260

GNI .911 .046 .893 19.712 .032

Balance of Payments -.151 .040 -.021 -3.750 .166

a. Dependent Variable: GDP


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21


dependent variable (GDP) and X is the independent variable (Exchange rates, Bank rates,

WPI, CPI, GNI and Balance of Payments). Hence, we arrive 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. Using this regression

equation to the entire series we find out the predicted values of GDP for the given values of

independent variables. These values shall be presented later in this section in the table 5.19

Table 4.19

ANOVA

Model Sum of Squares df Mean Square F Sig.

1 Regression 1.003E13 6 1.671E12 185498.790 .002a

Residual 9007333.846 1 9007333.846

Total 1.003E13 7

a. Predictors: (Constant), Balance of Payments, Bank Rates, Exchange Rate, CPI, WPI, GNI

b. Dependent Variable: GDP

Table 5.19 shows the anova table in which we find 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 we can look at is the level of significance the value of which is found to be 0.002. This

value is significant at 5% level of significance. Looking at the sum of squares, we find that the


(GDP). The detailed values of GDP at every level of independent variables are presented in the

table 5.20 below.

Table 5.20


Observe

d - Value

Predicte

d - Value

Residua

l

Standard

- Pred. v.

Standard -

Residual

Std.Err. -

Pred.Val

Mahalanobi

s - Distance

Deleted -

Residual

Cook's -

Distance

2002 1636037 1636037 0.00 -1.11418 0.000000 3001.222 6.125000

2003 1822468 1821307 1161.25 -0.95937 0.386926 2767.444 5.076953 7756.1 0.81125

2004 2090841 2090140 701.13 -0.73473 0.233613 2918.166 5.742926 12845.3 2.47412

2005 2452782 2455375 -2592.75 -0.42953 -0.863898 1511.789 0.901169 -3474.3 0.04858

2006 2938680 2938186 493.75 -0.02609 0.164516 2960.339 5.935589 18247.3 5.13798

2007 3578688 3578949 -261.25 0.50934 -0.087048 2989.837 6.071993 -34500.4 18.73507

2008 4410682 4410364 318.00 1.20408 0.105957 2984.350 6.046515 28362.1 12.61490

2009 4825085 4824905 180.00 1.55048 0.059976 2995.827 6.099854 50108.3 39.67971

Min. 1636037 1636037 -2592.75 -1.11418 -0.863898 1511.789 0.901169 -34500.4 0.04858

Max. 4825085 4824905 1161.25 1.55048 0.386926 3001.222 6.125000 50108.3 39.67971

Mean 2969408 2969408 0.02 0.00000 0.000005 2766.122 5.250000 11334.9 11.35737


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22

Medi

an2695731 2696781 249.00 -0.22781 0.082966 2972.344 5.991052 12845.3 5.13798



independent variables on the GDP. Besides presenting the predicted values of the series, table5.20 also presents the residual value, standardized predicted value, standard error of the predicted


GNI as a dependent variable

Table 4.21

Coefficientsa


Coefficients

t Sig.

B Std. Error Beta1 (Constant) 206911.824 259697.553 .797 .572

Exchange Rates 318.889 1994.159 .002 .160 .899

Bank Rates -4890.749 2050.417 -.004 -2.385 .253

WPI 910.217 694.910 .047 1.310 .415

CPI -3392.804 1672.082 -.149 -2.029 .292

GDP 1.095 .056 1.117 19.712 .032

Balance of Payments .164 .050 .023 3.299 .187

a. Dependent Variable: GNI


dependent variable (GNI) and X is the independent variable (Exchange rates, Bank rates, WPI,

CPI, GDP and Balance of Payments). Hence, we arrive at the regression equation GDP =

206911.82 + (318.889) Exchange Rates+ (-4890.749)Bank Rates + (910.217)WPI + (-3392.804)

CPI+ (0.164) Balance of Payments + (1.095) GDP. Using this regression equation to the entire

series we find out the predicted values of GDP for the given values of independent variables.

These values shall be presented later in this section in the table 5.22

Table 5.22

ANOVA

Model Sum of

Squares

df Mean Square F Sig.

1 Regression 9.645E12 6 1.608E12 148397.650 .002a

Residual 1.083E7 1 1.083E7

Total 9.645E12 7

a. Predictors: (Constant), Balance of Payments, Bank Rates, Exchange Rates, CPI, WPI, GDP

b. Dependent Variable: GNI

Table 5.22 shows the anova table in which we find the sum of squares, mean square, f statistic

and level of significance for regression equation as also for the residuals. The first important


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23

value that we can look at is the level of significance the value of which is found to be 0.002. This

value is significant at 5% level of significance. Looking at the sum of squares, we find that the


(GNI). The detailed values of GNI at every level of independent variables are presented in the

table 5.23 below.

Table 5.23


Observe

d -

Value

Predicted -

ValueResidual

Standard -

Pred. v.

Standard -

Residual

Std.Err. -

Pred.Val

Mahalanobi

s -

Distance

Deleted -

Residual

Cook's -

Distance

2002 1611994 1611994 0.00 -1.12107 0.000000 3291.318 6.125000

2003 1805933 1807137 -1204.38 -0.95482 -0.365925 3063.022 5.187599 -8993.6 0.92383

2004 2070109 2071006 -896.50 -0.73003 -0.272383 3166.860 5.605616 -12082.6 1.78238

2005 2422733 2419899 2833.75 -0.43281 0.860977 1674.103 0.936018 3822.8 0.04986

2006 2898256 2898750 -494.00 -0.02487 -0.150092 3254.051 5.967379 -21938.7 6.20429

2007 3539634 3539288 346.25 0.52080 0.105201 3273.043 6.047486 31268.5 12.75083

2008 4305650 4306024 -373.50 1.17399 -0.113480 3270.067 6.034900 -29017.7 10.96132

2009 4769271 4769483 -212.00 1.56881 -0.064412 3284.493 6.096002 -51176.1 34.39450

Min. 1611994 1611994 -1204.38 -1.12107 -0.365925 1674.103 0.936018 -51176.1 0.04986

Max. 4769271 4769483 2833.75 1.56881 0.860977 3291.318 6.125000 31268.5 34.39450

Mean 2927948 2927948 -0.05 -0.00000 -0.000014 3034.620 5.250000 -12588.2 9.58100

Medi

an2660495 2659325 -292.75 -0.22884 -0.088946 3262.059 6.001139 -12082.6 6.20429



independent variables on the GNI. Besides presenting the predicted values of the series, table

5.23 also presents the residual value, standardized predicted value, standard error of the predicted


5.4Stationarity and Causality Analysis of Indian Monthly data

Table 5.24

Exchange

Rates

WPI CPI

Valid N 96 96 96

Mean 103.9292 184.4419 186.0590

Median 102.7625 168.3500 167.2500

Mode 114.2580 233.5400 Multiple

Frequency 2 4 2

Minimum 93.3830 120.3540 129.6000

Maximum 116.9210 294.7000 277.6590

Variance 43.077 3036.495 2224.671


24/33

24

Std. Deviation 6.56329 55.10440 47.16642

Coef. Of variance 6.31516 29.87630 25.35025

Skewness 0.263307 0.529218 0.525176

Kurtosis -1.18682 -1.10259 -1.27886

As we did in the case of Indias data same as that Figure 5 to 8 present the line graphs of the

values of WPI, CPI and Exchange rates of Sri Lanka. While the return on WPI, CPI andExchange Rates are individually presented in figures 5 to 7, figure 8 presents common line

graphs for the three macro economic variables under study.

Figure 5

-.30

-.25

-.20

-.15

-.10

-.05

.00

.05

.10

.15

10 20 30 40 50 60 70 80 90

LOGWPI

Figure 6

-.16

-.12

-.08

-.04

.00

.04

.08

10 20 30 40 50 60 70 80 90

DCPI

Figure 7


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25

.06

.04

.02

.00

.02

.04

10 20 30 40 50 60 70 80 90

LOGEXCHNG

Figure 8

-.30

-.25

-.20

-.15

-.10

-.05

.00

.05

.10

.15

10 20 30 40 50 60 70 80 90

LOGCPI LOGEXCHNG LOGW PI

Figures 5 to 8 demonstrate the value of the three macro economic variables. It is indicated from

the figures that values at all the three macro economic variables are stationary in nature. In order

to further check the stationarity of the three series, we perform the Unit Root Test in order to

further confirm the same.

The unit-root 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 unit-root test and the augmented Dickey-Fuller test are

shown below in Table 5.25

Table 5.25


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26

Unit Root test on CPI

Augmented Dickey-Fuller Test Equation

Dependent Variable: D(LOGCPI_NA)

Method: Least Squares

Date: 03/23/11 Time: 10:55

Sample (adjusted): 3 95Included observations: 93 after adjustments


LOGCPI_NA(-1) -0.983429 0.125938 -7.808820 0.0000

D(LOGCPI_NA(-1)) 0.274317 0.101332 2.707104 0.0081

C 0.007146 0.002562 2.789463 0.0064





Log likelihood 219.9598 Hannan-Quinn criter. -4.632816F-statistic 34.22974 Durbin-Watson stat 2.100575



hypothesis can be rejected at 0.05 level of significance. It implies that LOG of CPI of Sri Lanka

is also a stationary one. Hence, the econometric models can now be applied on the series..

Table 5.26Unit root test on WPI


LOGWPI_NA(-1) -1.212027 0.101879 -11.89667 0.0000

C 0.008339 0.003974 2.098294 0.0386









hypothesis can be rejected at 0.05 level of significance. It implies that the LOG of WPI of Sri

Lanka is also a stationary one. Hence, the econometric models can now be applied on the series..

Table 5.27


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Unit Root Test on Exchange Rates


LOGEXCHNG_NA(-1) -0.792874 0.102070 -7.767954 0.0000

C 0.001687 0.001031 1.637207 0.1050

R-squared 0.396092 Mean dependent var -3.30E-05







The probability value of unit-root test in table 5.27, points towards the fact that the null

hypothesis can be rejected at 0.05 level of significance. It implies that the LOG of Exchange

Rate of Sri Lanka is also a stationary one. Hence, the econometric models can now be applied on

the series.

Table 5.28

Granger Causality test on Sri Lanka monthly data

Lags: 2

Null Hypothesis: Obs F-Statistic Prob.

LOGEXCHNG_NA does not Granger Cause LOGCPI_NA 93 0.86114 0.4262

LOGCPI_NA does not Granger Cause LOGEXCHNG_NA 0.08428 0.9193

LOGWPI_NA does not Granger Cause LOGCPI_NA 93 2.11268 0.1270

LOGCPI_NA does not Granger Cause LOGWPI_NA 0.49521 0.6111

LOGWPI_NA does not Granger Cause LOGEXCHNG_NA 93 1.12065 0.3307

LOGEXCHNG_NA does not Granger Cause LOGWPI_NA 1.07596 0.3454

Table 5.28 presents the results about the application of Grangers Causality model to the WPI,

CPI and Exchange Rates of India. Null hypothesis in the case of Grangers causality model is

that A does not granger cause B. On those lines, table 5.28 tests the hypotheses about the

three variables in pairs. The results 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 & LOGCPI and LOGWPI & LOGEXCHNG.

Now we apply the Vector Auto Regression (VAR) model on the series under reference in order

to furtherconfirm the results produced by the Grangers Causality model.

In table 5.29, we present the application of Vector Auto Regression (VAR) Model at the three

stock exchanges.

Table 5.29


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Vector Auto Regression test on Sri Lankas monthly data

Vector Autoregression Estimates

Date: 03/11/11 Time: 16:30

Sample (adjusted): 3 95

Included observations: 93 after adjustments

Standard errors in ( ) & t-statistics in [ ]

LOGCPI_NA LOGEXCHNG_NA LOGWPI_NA

LOGCPI_NA(-1) 0.276479 0.015195 0.141757

(0.10083) (0.04376) (0.17074)

[ 2.74199] [ 0.34725] [ 0.83027]

LOGCPI_NA(-2) -0.296388 0.018369 -0.041587

(0.10047) (0.04360) (0.17012)

[-2.95002] [ 0.42132] [-0.24445]

LOGEXCHNG_NA(-1) 0.369838 0.201905 0.165298

(0.24486) (0.10626) (0.41462)[ 1.51039] [ 1.90010] [ 0.39868]

LOGEXCHNG_NA(-2) -0.160170 -0.036185 0.493158

(0.24607) (0.10678) (0.41666)

[-0.65092] [-0.33886] [ 1.18360]

LOGWPI_NA(-1) 0.069514 -0.032033 -0.222476

(0.06317) (0.02741) (0.10696)

[ 1.10047] [-1.16856] [-2.07999]

LOGWPI_NA(-2) 0.133537 -0.034492 0.014252

(0.06327) (0.02746) (0.10713)

[ 2.11064] [-1.25627] [ 0.13303]

C 0.005549 0.001896 0.006310

(0.00265) (0.00115) (0.00449)

[ 2.09272] [ 1.64751] [ 1.40530]

R-squared 0.186419 0.073080 0.076002

Adj. R-squared 0.129657 0.008411 0.011537

Sum sq. resids 0.044597 0.008399 0.127867

S.E. equation 0.022772 0.009882 0.038559

F-statistic 3.284250 1.130059 1.178971

Log likelihood 223.4234 301.0607 174.4440

Akaike AIC -4.654267 -6.323886 -3.600946

Schwarz SC -4.463642 -6.133260 -3.410320

Mean dependent 0.007250 0.002034 0.007032

S.D. dependent 0.024410 0.009924 0.038784

Determinant resid covariance (dof adj.) 7.47E-11

Determinant resid covariance 5.91E-11

Log likelihood 699.2882

Akaike information criterion -14.58684

Schwarz criterion -14.01497


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By the application of VAR Model, we observe that the integration of macroeconomic variables

with the other can be established if the p-value is more than 1.96. Table 5.29 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. In LOGWPI at

the lag 1 does not have any influence on LOGCPI and LOGEXCHNG but it influences the return

at LOGWPI in period 0. In LOGWPI at lag 2 LOGWPI have influence on LOGCPI but it does

not influence LOGEXCHNG and LOGWPI.

Table 5.30

Variance Decomposition on Sri Lankas monthly data

Variance Decomposition of LOGCPI_NA:

Period S.E. LOGCPI_NA LOGEXCHNG_NA LOGWPI_NA

1 0.022772 100.0000 0.000000 0.000000

2 0.024161 96.51089 2.261100 1.228008

3 0.025031 93.01309 2.117113 4.869802

4 0.025200 92.95173 2.168757 4.879512

5 0.025229 92.76467 2.180776 5.054553

6 0.025245 92.75595 2.185437 5.058613

7 0.025246 92.75185 2.185334 5.062813

8 0.025247 92.74894 2.186027 5.065036

9 0.025247 92.74892 2.186094 5.064990

10 0.025247 92.74873 2.186116 5.065150

Variance Decomposition of

LOGEXCHNG_NA:


1 0.009882 0.544881 99.45512 0.000000

2 0.010166 0.697640 97.82953 1.4728293 0.010253 0.856835 96.17923 2.963936

4 0.010256 0.857207 96.16613 2.976659

5 0.010260 0.867678 96.15021 2.982117

6 0.010260 0.870224 96.14645 2.983325

7 0.010260 0.870247 96.14643 2.983320

8 0.010260 0.870455 96.14619 2.983358

9 0.010260 0.870466 96.14618 2.983358

10 0.010260 0.870471 96.14617 2.983359

Variance Decomposition of LOGWPI_N


1 0.038559 0.225725 0.001937 99.77234

2 0.039644 0.763915 0.178602 99.057483 0.040088 0.749457 1.943572 97.30697

4 0.040104 0.766544 1.947060 97.28640

5 0.040110 0.767313 1.946547 97.28614

6 0.040110 0.768456 1.946880 97.28466

7 0.040110 0.768456 1.947305 97.28424

8 0.040110 0.768561 1.947306 97.28413

9 0.040110 0.768578 1.947306 97.28412

10 0.040110 0.768580 1.947308 97.28411


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Cholesky Ordering: LOGCPI_NA

LOGEXCHNG_NA LOGWPI_NA

Finally, the Variance Decomposition Analysis of the three macro economic variables is

presented in the table 5.30. The table decomposes the values at the three macro economic

variables for a period ranging from 1 to 10.The Variance Decomposition Analysis as presented in table 5.30. 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 aim of this research is to find out the impact of macroeconomic variables on GDP growth

and study the pattern of CPI, WPI, GDP, GNI and Rate of interest in India and Sri Lanka. To

solve this basic purpose monthly data was used from 2002 to 2009 of both of the countries and

the basic and believed to be indicator variables were used and studied and analyzed by first

applying the basic statistical tools like correlation and descriptive statistical tools and finally

regression, unit root test, Granger causality, VAR and Variance decomposition models.

The application of Unit-root test (Augmented Dickey-Fuller test) reveals that the null hypothesis

can be rejected at 0.05 level of significance. It implies that the series of WPI, CPI and Exchangerates of India and Sri Lanka are stationary. Grangers Causality Model when applied to the three

series indicates thatprobability value for the hypotheses Exchange rate does not Granger Cause

LOGCPI and LOGCPI does not Granger Cause LOGEXCHNG in Indian data and 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 Sri

Lankas data. None of the other variables happen to Granger cause any of the other variables

under study. The application of the VAR model implies 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 influence 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 in Indian data and in the case of Sri Lankas data

Vector Auto regression (VAR) model implies that 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. In LOGWPI at the lag 1 does not have any influence on


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LOGCPI and LOGEXCHNG but it influences the return at LOGWPI in period 0. In LOGWPI at

lag 2 LOGWPI have influence on LOGCPI but it does not influence LOGEXCHNG and

LOGWPI. The Variance Decomposition Analysis 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, 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. In case of Sri Lanka data 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.

After applying all the models on the data of both the countries the results do not lead us to any

clear-cut conclusion because the results from all the models are different. Granger model and

VAR model indicates that LOGCPI, LOGWPI and LOGEXCHNG does not have any influence

on each other in the case of both of the countries but the Variance decomposition model shows

visible impact of macroeconomic variables on each other in some of the cases in Indian and Sri

Lankan data which is mention above.

References


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