INTRODUCTION:

Persistently the relationship between money supply, price and economic growth is a very significant subject in the field monetary economics. In view of the importance of economic growth, steady concern has been given by many monetary economists to the relationship of money supply, price and economic growth. In spite of the importance of the subject, economists dissent on the impact of money supply on economic growth. Money supply, Income and prices are significant macro variables which play significant role in an economy. An understanding on the relationship between these macroeconomic variables is of significant importance, mainly to policymakers in ensuring that effective macroeconomic policies can be formulated and implemented. There has been an extensive debate in economics pertaining to the role of money in the determination of prices and income.

The Monetarists argue that money plays an active role and leads to changes in income and prices. In other words, changes in income and prices in an economy are mainly caused by the changes in money supply. Therefore, the direction of causation runs from money supply to income and prices is unidirectional. On the other hand, the Keynesians argue that money does not play an active role in changing income and prices. In fact changes in income cause changes in money supply through demand for money which implies that the direction of causation runs from income to money supply without any feedback. Similarly, changes in prices are mainly caused by structural factors. Though there is difference among economists on the roles of income, money supply, and prices and their interrelationship, these variables are considered important and large amount of literature in economics deals with these macroeconomic variables. In particular, the causal relationships between money supply and income and between money supply and prices have been an active area of investigation in economics particularly after the provocative paper by Sims (Ahmed, Suliman, 2011).

REVIEW OF LITERATURE:

Joshi and Joshi (1990) found that real value of money stock indicates correctly the appropriate monetary stance because the direction of monetary policy in terms of nominal magnitudes is highly misleading. Rangarajan and Arif (1990) have found that the policy simulations show that while a substantial increase in government capital expenditure increases output, its impact on output and prices also depend on the extent of the resource gap met by borrowing from RBI. Rangarajan (1998) modelled the relationship between money, output and prices covering the period 1970-71 to 1992-93 and established that it was possible in the Indian context to predict the average inflation rate in the medium term on the basis of the reduced form money demand equation. Bhattacharyya and Sensarma (2005) found that instantaneous impact of monetary policy signals on most financial markets points towards increasing integration and sophistication of markets. The signaling role of monetary policy is effective in case of India, empirical evidence favours rate channel. Sharma (2008) explained the empirical results of cointegration which showed that both narrow and broad money do affect real output at seasonal frequencies but at zero frequency, which means in the long run, money does not affect real output. Money is neutral in the long run, but not in the short run. Jeevan K. Khundrakpam and Rajan Goyal (2008) found that money and real output cause price both in the short as well as in the long run while money is neutral to output. Mohanty (2010) has attempted to describe the monetary policy framework in India: experience with multiple indicators approach. The study found that this improved performance of monetary policy was facilitated by the supportive fiscal policy. Mishra, Mishra and Mishra (2010) investigated the dynamics of the relationship between these macro-economic aggregates for India over the period 1950-51 to 2008-09. The study indicates the existence of long-run bidirectional causality between money supply and output and unidirectional causality from price level to money supply and output. But, in the short-run the bidirectional causality exists between money supply and price level and unidirectional causality exists from output to price level. The results infer that money is not neutral. Gaurang Rami (2010) strongly supports the monetarists view and partially supports the Keynesian view. Yadav and Lagesh (2011) revealed that there exists a long-run relation between real output, money supply, interest rate and exchange rate when the price variable was the dependent variable. Singh, Das and Baig (2015) tried to re-examine relationship between money supply, output and prices in the short and long-term. Different metrics for money, output and prices are used to understand the relationship between each. Variables to understand food inflation is especially used considering the fact that food prices are less income elastic and are viewed differently by citizens. The findings indicate that the relationship is sensitive to the choice of variable. Asghar OsatiEraghi (2015) has been observed that monetary policy is effective to control both prices and output in long-run as well as in short-run.

Research Gap:

This study will add to the body of existing literature by examining the money, prices and economic growth relationship in long-run and short-run in India. The study considers Consumer Price Index as measure of price level. Since in recent days Reserve Bank of India (RBI) inclined to choose Consumer Price Index (CPI) over Wholesale Price Index (WPI) as a better inflation indicator, this study considers CPI as proxy variable to measure Price level.

OBJECTIVE:

The major objective of this paper is to test short run and long run relationship between Economic growth (real GDP), money supply (M₃) and price (CPI).

Hypothesis:

1) H₀: There exists no short run relationship between Money supply price level and economic growth.

2) H₀: There exists no long run relationship between Money supply price level and economic growth.

METHODOLOGY:

Data Description and Source:

The time period of the study will be from 1971 to 2015. The main source of data is the Handbook of Statistics on Indian economy, RBI and labour bureau statistics. The macroeconomic data under examination consists of real Gross Domestic Product with base 2004-05 (Real GDP), money supply and price level. The measure of money supply used is the broad one. As far as prices are concerned, Consumer Price Index (CPI) is used to represent the movements in prices.

Econometric Methods:

The Augmented Dickey-Fuller (ADF) unit root test is employed as a prior diagnostic test to examine the stochastic time series process properties of monetary, price and output data. Once a unit root has been confirmed for a data series, the next step is to examine whether there exists a long-run and short-run equilibrium relationship among variables.

The Engle-Granger Two-Step Modelling Method (EGM):

Among a number of alternative methods, the EGM, originally suggested by Engle and Granger (1987), has received a great deal of attention. One of its benefits is that the long-run equilibrium relationship can be modelled by a regression involving the levels of the variables. This model follows two steps. In the first step, all dynamics are ignored and the cointegrating regression is estimated by the Ordinary Least Squares (OLS). The long-run (cointegrating) regression can be written as:

Y_t = bX_t + u_t(1)

where both Yt and Xt are non stationary variables and integrated of order one i.e. Y_t ~ I(1) and X_t ~I(1). In order for Y_t and X_t to be cointegrated, the necessary condition is that the estimated residuals from Eq. (1) should be stationary i.e. u_t ~ I(0). Since the variables in Eq. (1) are non stationary.

The second step involves estimating a short-run model with an error-correction mechanism (ECM) by the OLS. According to the Granger Representation Theorem (GRT), if a number of variables, such as Y_t and X_t, are cointegrated, then there will exist an ECM relating these variables and vice versa. Conditional on finding cointegration between Y_t and X_t, the estimate of b from the first step long-run regression (1) may then be imposed on the following sort-run model with the remaining parameters being consistently estimated by the OLS. In other words, we retrieve the estimate of b from Eq. (1), and insert it in place of b in the error-correction term (Yt-bXt) in the following short-run equation:

DY_t = a₁DX_t + a₂(Y-bX)_t-1 + e_t (2)

where D represents first-differences and et is the error term. Alternatively, in practice, since Y_t-bX_t = u_t, one can substitute the estimated residuals from Eq. (1) in place of the error-correction term, as the two will be identical. The estimated coefficient a₂ in the short-run Eq. (2) should have a negative sign and be statistically significant. To avoid an explosive process, the coefficient should take a value between -1 and 0. According to the GRT, negative and statistically significant a₂ is a necessary condition for the variables in hand to be cointegrated. In practice, this is regarded as a convincing evidence and confirmation for the existence of cointegration found in the first step. It is also important to note that, in the second step of the EGM, there is no danger of estimating a spurious regression because of the stationarity of the variables ensured. Combinations of the two steps then provide a model incorporating both the static long-run and the dynamic short-run components.

EMPIRICAL RESULTS AND DISCUSSION:

At an important step of time series empirical analysis requires to determine the order of integration for each of the three variables used in the analysis. The ADF unit root test has been used for this purpose and the results of this test are reported in Table-1. And, it is clear that the null hypothesis of no unit roots for all the time series are rejected at their first differences since the ADF test statistic values are less than the critical values. Thus, according to ADF test the variables are stationary and integrated of same order, i.e., I(1).

Table 1: Testing Unit-Root Using Augmented Dickey-Fuller Test

Variables

Level

First Difference

Decision about Order of Integration

Intercept

Trend and Intercept

None

Intercept

Trend and Intercept

None

Ln(real_gdp)

3.04

(1.00)

-2.05

(0.55)

12.38

(1.00)

-5.94***

(0.000)

-7.32***

(0.000)

-0.560

(0.46)

I(1)

Ln(CPI)

-0.51

(0.87)

-1.79

(0.69)

2.87

(0.99)

-4.56***

(0.001)

-4.55***

(0.003)

1.47

(0.12)

I(1)

Ln(M3)

-0.72

(0.82)

-2.15

(0.50)

1.07

(0.92)

-5.45***

(0.000)

-5.48***

(0.000)

-0.63

(0.43)

I(1)

*** - 1% level of significance

Table 2: Regression Result

Variable	Coefficient	Std. Error	t-Statistic	Prob.
C	-1.434683	0.213593	-6.716900	0.0000
M3	0.694543	0.112459	6.175973	0.0000
CPI	1.166374	0.233059	5.004639	0.0000
R-squared	0.997591	Mean dependent var		7.762587
Adjusted R-squared	0.997473	S.D. dependent var		2.568205
S.E. of regression	0.129099	Akaike info criterion		-1.190735
Sum squared resid	0.683324	Schwarz criterion		-1.069085
Log likelihood	29.19616	Hannan-Quinn criter.		-1.145621
F-statistic	8488.038	Durbin-Watson stat		0.351019
Prob(F-statistic)	0.000000

Table 4: VAR Lag Order Selection Criteria

Lag	LogL	LR	FPE	AIC	SC	HQ
0	-12.90829	NA	0.000436	0.776014	0.901398	0.821672
1	241.9285	459.9494*	2.71e-09*	-11.21603	-10.71449*	-11.03339*
2	250.9336	14.93522	2.73e-09	-11.21627	-10.33859	-10.89667
3	260.1063	13.87097	2.76e-09	-11.22470*	-9.970865	-10.76812
* indicates lag order selected by the criterion
LR: sequential modified LR test statistic (each test at 5% level)
FPE: Final prediction error
AIC: Akaike information criterion
SC: Schwarz information criterion
HQ: Hannan-Quinn information criterion

The appropriate lag length is 3 based on the minimum AIC value.

Granger Causality Test Result:

The result of the Causality test is presented in the following table.

Table 5: Pairwise Granger Causality Tests

Null Hypothesis:	Obs	F-Statistic	Prob.
M3 does not Granger Cause CPI	41	3.86343	0.0176
CPI does not Granger Cause M3		1.28955	0.2937
REAL_GDP does not Granger Cause CPI	41	9.97555	0.0007
CPI does not Granger Cause REAL_GDP		1.68011	0.1896
REAL_GDP does not Granger Cause M3	41	1.32748	0.2815
M3 does not Granger Cause REAL_GDP		0.49300	0.6895

The direction of causation between real GDP and prices was found to be uni-directional from real GDP to CPI without any feedback. Regarding the causal relationship between money and prices, the analyses suggests that the causation runs from money supply to prices, but price level does not causes money supply. The reasons behind this scenario are that, in one hand government is borrowing from the banking system which contributed to inflation. On the other hand, financial markets are not well developed and their influence in the economy as a whole is not strong enough. Moreover, the majority of the people do not have adequate knowledge and much confidence in the financial markets. Hence, it seems that the main alternative to holding money is spending on goods and services and therefore causes the movement in prices.

Step 2: Estimating a short-run model:

The second step involves estimating a short-run model with an error-correction mechanism (ECM) by the OLS. The result of short-run model has been presented in the below table. The estimated coefficient Error Correction Term in the short-run model should have a negative sign and be statistically significant to say that there exists short term relationship between the variables. The result of Error Correction Model has been presented below:

Table 6: Error Correction Model Result

Variable	Coefficient	Std. Error	t-Statistic	Prob.
D(M3)	1.253188	0.109175	11.47868	0.0000
D(CPI)	-0.081060	0.199660	-0.405992	0.6870
ECT(-1)	0.591203	0.161036	3.671241	0.0007
R-squared	0.435422	Mean dependent var		0.192409
Adjusted R-squared	0.406470	S.D. dependent var		0.070830
S.E. of regression	0.054568	Akaike info criterion		-2.909985
Sum squared resid	0.116129	Schwarz criterion		-2.785866
Log likelihood	64.10968	Hannan-Quinn criter.		-2.864490
Durbin-Watson stat	1.972426

Though the coefficient of the ECM term is significant it is positive. This means that GDP is too high to be in equilibrium. If there is any short term disturbance from the long run stable relationship, such a disturbance would not be corrected over time and the long-run stable relationship would not be restored.

CONCLUSION:

This study has attempted to analyze the short run and long run relationship between money supply, price and output. The co integration test shows that price level (CPI), money supply and real GDP are cointegrated. This implies that the variables have long run equilibrium relationship. Also the long-run equilibrium relationship shows that money supply and CPI positively affects the real GDP in the long run. Following the co integration result, causality test is formed and the result indicates that there exists a long run causality running from economic growth to Price and also there exists long run causality running from money supply to price. The Error Correction Model results represents that there is no short run relationship among the variables.The Keynesian views that money does not play an active role in changing income and price is justified by our results in the short run. However, the monetarist view that money (broad Money) plays an active role and leads to change in income in India during long run is not supported very clearly in our findings.

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Received on 17.04.2018 Modified on 05.06.2018

Res. J. Humanities and Social Sciences. 2018; 9(3): 580-584.

DOI: 10.5958/2321-5828.2018.00097.9

	Constant
ADF	t-test	Prob
Test Critical Values	-1.94	0.04