A Study of Interlink-age of Selected Macroeconomic Variables with GDP Growth Rate in India

A Study of Interlink-age of Selected Macroeconomic Variables with GDP Growth Rate in India

Hemlata Tiwari / Volume 13/ Issue 2/ (April-September 2020)


GDP stands for "Gross Domestic Product" the total monetary value of all final goods and services produced (and sold in the market) with in a country in an accounting year. It is a very vital sign to indicate health of an economy. It is the sum of all the produce of a country which comprises of all purchases of goods and services produced by a country and services used by individuals, firms, foreigners and the governing bodies. To find out the major factors affecting the GDP growth of a country, it is required to examine the impact of various macroeconomic variables on GDP. Purpose: This paper is an attempt to explain the concept of Gross Domestic Product (GDP) to layman & its importance for an economy. In this article, we have tried to establish relation among GDP, Consumer Price Inflation (CPI) rate, foreign institutional investment (FII) & domestic institutional investment (DII). 

Method: This paper tried to explore the relation among the variables. To find out the relation among the variable EVIEWS software student version -10 has been used, because all the selected variables belong to time series data first descriptive statistics has been calculated, in which we find out normality by Jerque – bera statistics, distribution by skewness & peakedness by kurtosis. Once we find out normality among the data series we will check for stationarity by applying various unit root tests among data series. To find out linkage among various data series, Engle – Granger cointegration test has been applied to find relation among various data series. Vector auto regression (VAR model) has been applied to find impact of exogenous variables on endogenous variable. To predict cause & effect relation among the different variables granger causality test has been applied. 

Results: By application of these statistical & econometric tools, we can predict the characteristic of different data series & establish relation among the data series. 

Conclusion: This study will help to make a model to predict the movement of different variables covers under study & their influence on the movement of other variables. 

Keywords: CPI, DII, GDP, FII, Economic Growth


GDP are used as an indicator by almost all the governments and economic decision-makers for planning and policy formulation. It illustrates the overall progress made by the country people. Whenever there is a fall in any sector or industry, fall in the income of the companies push them for cost cutting which

again affects the employment of that sector. On the contrary, when there is an upswing sector, companies in that sector try to gain leverage by investing more in infrastructure & human resource to capture the market. While on the other side it creates demands for other

sectors through its backward & forward association. Though, in case of boom more perquisites are given to higher management then common man while in case of recession more layoff is from common people than higher management. Apart from this industry specific fallout, fall in growth rates results in fall in public revenue also which further causes fall in public investment. It is adversely affecting the important public services. To fill the gap of revenue & expenditure, government may lie upon fiscal deficit. The excess fiscal deficit is also inflationary in nature. Thus, higher GDP growth rate is important not for the economic health of the economy but also for the economic health of the common man either directly or indirectly. Though, with 6% growth rate too we are not able to make the fruit of growth penetrate up to every section of the population, it does not mean that we don’t need growth but we need more quantum of growth & other factors of development like equity, health, welfare is not less important but these factors will not meet the desired objective if there is no growth. Here the question arises how to increase our GDP growth rate. Attracting foreign investors towards our economy is one of the most lucrative solutions of this question. Foreign investors use to invest in other countries stock market specifically in the developing economies with strong fundamentals in greed to get higher return on their investment. This kind of investment is known as Foreign Institutional Investors (FII). India is one of the fastest growing economy with strong fundamentals seems a lucrative option to foreign investors to park their money to get higher return. India’s current account deficit (CAD) is high & it will take a few years for this value to decrease significantly, this is the reason that government is giving emphasis to increase in FII.

Thus, it becomes important to understand the nature & direction of relationship between the economic growth of a country and significance of FII, Domestic Institutional Investors (DII) & Consumer Price Inflation (CPI) in economic growth of nation. This study considered GDP, FII, DII & CPI data to find the interlink age among different variables. 


1. To find out the significance of relation between GDP & selected macroeconomic variable. 2. To explore the cause & effect relation among all the pairs of various variables. 

3.0 HYPOTHESIS H o1 : 

There is no significant relationship among GDP, FII, DII & CPI inflation rate. H 11 : There is significant relationship among GDP, FII, DII & CPI inflation rate. H 02 : There is no cause effect relationship among GDP, FII, DII & CPI inflation rate. H 12 : There is cause effect relationship among GDP, FII, DII & CPI inflation rate. 


Several scholars have already written research article on influence of various macroeconomic variables on GDP. Some of them are as follows: Yadav Dr. Satyendra Kumar & Yadav B. Kumar (June, 2016) has tried to explain FII’S are the cause for the changes in the values of majority of macroeconomic variables, rather than an effect. Pant Sumi & Bansal Mohit (2015) has written a scholar write up & explained both FDI and FII are essential for financial growth, however, FDI plays a more significant role than FII in the progress of any developing country especially like India. FDI improve the country not only with the influx of foreign funds but also outcome in the transfer of superior technology and skills, thus improving job opportunities. Dina Abdel Moneim Rady (November 3, 2010) has compared GDP, inflation rate & other

International Journal of Business Insights and Transformation

macroeconomic variables of two countries. He stated that Egypt and India are both emerging developing countries which share some common economic features; their rate of economic growth has approximately the same trend while they both begin series of economic reform process in the same period (early 1990’s) while passing through high inflation rates. Kashyap kumar Suresh explained relationship between the stated variables. He explained this relationship with the help of augmented dickey fuller test & granger causality. Zaria Yusuf Bala has investigated the significant influence of FDI on GDP growth in Malaysia both in the short- and long-run perspectives. From the above stated studies, it is clear that various macroeconomic variables has some relation with GDP of any nation that why some of these variable like FII, DII, CPI variables are considered for this study.


This study is based on secondary data. To explore the relation among the variable EVIEWS software is used, by this software we can apply various statistical & econometric tools to study time series data. To start this study data of all the stated variables from year 2008 till 2019 has been collected, the data related to various variables has been collected from different websites like rbi.org, money control.com & inflationdata.com.

YEAR GDP Growth % at 2011-12 price CPI FII (IN INR CRORE) DII (IN INR CRORE)
2008 7.66 8.32% -101802.57 72966.78
2009 3.09 10.83% 24820.09 26106.16
2010 7.86 12.11% 61225.25 -18632.06
2011 8.5 8.87% -26957.041 29482.129
2012 5.24 9.30% 101166.11 -55800.09
2013 5.46 10.92% 87358.79 -72370.68
2014 6.39 6.37% 73656.42 -29648.3
2015 7.41 5.88% -16435.56 64653.11
2016 8 4.97% -11838.65 40080.69
2017 8.17 2.49% -41121.63 89211.41
2018 7.17 4.85 -66869.3 96645.92
2019 6.81 7.39% 10146.681 56010.418


Descriptive statistics of selected variables (Table -1) has shown different measures of central tendency, dispersion, distribution (skewness & kurtosis). It is clear from the table that some variables like GDP, FII, DII are negatively skewed while CPI is positively skewed. There is high degree of variability observed in case of standard deviation of FII & DII while very low variability observed in case of GDP & CPI. GDP & CPI are leptokurtic while FII & DII are mesokurtic. The Jarque-Bera (JB) statistics told about goodness of fit of the data.

 Ho: The distributions of time series are normally distributed. H 1 : The distributions of time series are not normally distributed. 

The tabulated values of p-values are more than 0.05 except CPI; therefore, accept Ho for all distributions except CPI at 5% level of significance. This implies that the distributions of macroeconomic variables are symmetrical about their mean values.

Test of Stationarity This test is use to show the presence of unit root in data series, if unit root is there it implies that data is not stationary. Stationarity means there is no change in the basic properties of distribution like mean, variance and covariance constant over the period. Hypothesis for this test is as follows. 

Ho: There is unit root in the data. (Non-stationary; δ = 0) H 1 : There is no unit root in the data. (Stationary; δ < 0) 

The finding of various unit root test (Table-2) has shown that P value is less than 0.05 for various unit root test, therefore reject null hypothesis & it prove stationarity of data series. Again, pattern of different data series in Graph-1 has shown that data is stationary.

Cointegration Test

To find out the relation among different data series we use to apply cointegration test. Here yearly data of different variable has been collected so data series has few numbers that why Engle- Granger cointegration test use to

apply to find out the relation among different time series. The hypotheses for cointegration test are as follows.

Ho: There is no cointegration among the different data series. H1: There is cointegration among the different data series. In this test of cointegration τ (t- statistics) is used to determine acceptance or rejection of null & alternate hypothesis. If | τ | > | Engel Granger Critical Value at 5% |, then reject null hypothesis The findings of Engle – Granger cointegration has shown (Table-3) that all the variables series has accepted null hypothesis of no cointegration &rejected alternate hypothesis of cointegration in the data series.

Vector Auto Regression 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. This model takes different model for all the four variables (Table -4). In each of four models it takes one variable as endogenous & rest as exogenous. In this way it uses to estimate impact of the entire variable on one variable.

Here in Table - 5 probability values of all the 36 coefficients has been calculated by least square but unfortunately it is more than 5%. It indicates no significance of any independent variable in defining the dependent variable. To find the joint significance of 2 lag value of any dependent variable Wald Coefficient Test has been applied in all system model equations. The result of Wald test has shown that in model 3 (where FII is dependent variable) FII & GDP exogenous variable has significant impact on it while in model 4 (where GDP is dependent variable) CPI has significant impact of it. In rest of the model other rest variable has shown no significant impact on dependent variable.

Granger – Causality 

Test To find out the cause & effect relationship among the different variable granger causality test has been applied. This test is use to establish the cause & effect relationship among the data series. The hypothesis of granger causality is as follows. 

H O : X does not cause Y., H 1 : X causes Y. 

Table – 6 has shown either null hypothesis got accepted or rejected. As a thumb rule if P value is more than 5% then reject null hypothesis & accept alternate hypothesis. The finding of cause & effect relationship between different pair of variables has shown that in only in model -3 (where FII is dependent variable) null hypothesis of no causality rejected, it implies FII causes CPI, DII & FII while reverse is not true in case of any variables all the pairs has rejected alternate hypothesis of causality.


Here the analysis of normality & stationarity test is shown that all the data series is normal & stationary. Cointegration test statistics has

shown no cointegration in data series, while finding of vector auto regression has shown 4 models of different variables. Ordinary least square has given the probability value of all 36 coefficients of system model equations. Findings of Wald test has shown that only FII & GDP is significant for predicting movement in FII & only CPI is suitable to predict GDP out of four variables considered in this study. The findings of granger causality have shown that only FII has impact on other variables of this study while rest of the variables does not have any cause – effect relationship. This study has shown a model for future researchers to include number of variables & analysis there impact on each other. This study will help in predicting the behavior of any variable. 


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