A study of causality between money supply and price level in india (monthly data): 1953 to 2005

Transcription

1 International Conference on Applied Economics ICOAE A study of causality between money supply and price level in india (monthly data): 1953 to 2005 Medhavin Bhalchandra Dave 1, Gaurang Dattubhai Rami 2 Abstract The debate on the role of money supply in the determination of nominal income and price has remained one of the important issues in the history of economic thought. The Monetarists and the Keynesians have diametrically opposite views on this important issue; the Monetarists firmly believe that changes in money stocks change nominal income as well as prices while the Keynesians think that money supply does not play any important role in the determination of nominal income and prices. The empirical study undertaken so far to understand these relationships have been inconclusive. In this paper an attempt has been made to determine the direction of causality between money supply and prices. We have taken monthly data on two alternative measures of Money supply viz. Narrow Money (M 1 ) and Broad Money (M 3 ) and a measure of Price Level viz. the Whole Sale Price Index (WPI) for the Indian economy from June 1953 to December Before applying the Granger s test of causality, we have used the ratio-to-moving average multiplicative method to deseasonalize the series and have converted them into natural logarithmic form for reducing variations in them. We have also used the Augmented Dickey Fuller (ADF) test for this purpose. For deciding about the lag order, 5 criteria viz. LR, FPE, AIC, SC and HQ have been used. Then pair wise Granger Causality tests have been performed. Our results have been compared with the conclusions drawn in the earlier studies and it was found that our main conclusion is consistent with the conclusions of the majority of the studies that there exists reverse Granger Causality i.e. price to money supply as far as the Indian economy is concerned. Key Words: Pair wise Granger Causality Test, Augmented Dickey Fuller (ADF) test, India JEL Codes: C22, E31, E51 1. Introduction The debate on the role of money in an economy particularly in the determination of nominal income and prices has occupied central place in history of economic thought. The Monetarists claim that money plays an important role and leads to changes in nominal income and prices. In other words, changes in nominal income and price in an economy are mainly caused by the changes in money stocks. Hence, the direction of causation runs from stock of money supply to nominal income and prices without any feedback. This is known as unidirectional causation. The Keynesians, on the other hand, argue that stock of money supply does not play an active role in changing income and prices. In fact, changes in income cause changes in money stock via demand for money implying that the direction of causation runs from income to money without any feedback. They agree that changes in prices are mainly caused by the structural factors. The existing literature on the empirical studies on the relationship between money, income and price is inconclusive. Moreover, the papers reviewed here investigated causality between two variables, i.e. between money and income and/or between money and prices. However, an economic variable is generally influenced by more than one variable; therefore, models involving more variables may be more useful. 2. Review Of Literature In India, perhaps the earliest attempt in this respect is due to Ramchandra (1983). He used yearly data for the period , to study the causality between money on one hand and both output (real and nominal) and prices on the other. The following variables were taken into consideration in studying the relationship. (a) NNP at current and at prices (b) Narrow money, M 1 as annual average monthly values (c) Implicit NNP deflator with base The study used Sims test with independent variables involving past, present and one future lags. The methodology adopted for detecting causality was rather simplistic. If an equation was found to be satisfactory in terms of R 2 and Durbin Watson [DW] statistic and the coefficient of X t (and its differences) are found to be statistically significant individually, then the study concluded that X is causing Y. On the basis of regression results the study arrived at the following conclusions: (a) Money causes both real income and price level (b) Price level causes real income (c) Nominal income cause money The first elaborate causality study on money-output-prices in India seems to have done by Nachane and Nadkarni (1985). Their study presented an exhaustive theoretical survey of various causality tests. Spanning over the period to and based on quarterly data, they employed four tests viz., (i) (ii) (iii) (iv) Sims Hsiao s Final Prediction Error (FPE) Cross-correlation test and Transfer function test 1 M B Dave, Reader, Department of Economics, Veer Narmad South Gujarat University, Surat Gujarat INDIA medhavin55@gmail.com, medhavin55@rediffmail.com 2 Gaurang Rami, Lecturer, Department of Economics, Veer Narmad South Gujarat University, Surat Gujarat INDIA, grami@rediffmail.com, gramee@sgu.ernet.in

2 246 International Conference on Applied Economics ICOAE 2008 They measured output by GNP and price level by WPI. As far as the monetary variables are concerned, both M 1 and M 3 were taken into account. Stationarity of data and the related truncation process for selecting the lag structure was achieved through stepwise autoregression with the help of Box s Q-Statistic for the Sims test. The results were reinforced by the use of Akaike s (1976) Information Criterion (AIC). Their findings on the money-output-prices nexus turned out to be independent of the type of test adopted for detecting the direction of causation. All their test results demonstrate that money supply is a major determinant of nominal national income in India, though for M 3 the conclusion seems to be sensitive to the methods used to construct quarterly estimates of national income. There is uniformity among the test results in suggesting a unidirectional causality from money stock to prices; though in the case of M 3 the effect is evident only for longer lags (in excess of 16 quarters). On the other hand, as far as money supply affecting real output is concerned, their tests remained inconclusive. Despite the technical finesse and the exhaustiveness of the test procedures Nachane and Nadkarni pointed out some limitations of the database. Their construction of quarterly estimates of national income should be treated with some caution, particularly in view of the fact that their conclusions often turn out to be sensitive to the type of estimate of national income used. Sharma, Ram Lal (1985) while using the Sims methodology to test causality between Money and Price level in India observed that: 1) For the sample period the causality from M 1 to P was much stronger than the reverse causality from P to M 1. 2) Bidirectional causality existed between M 3 and P However in his later study (1987) the author has re-examined the issue of causality between Money and Price level in India and has used only Granger s test for this purpose. Sharma has found the existence of unidirectional causality running from M 1 to P on the one hand and from M 3 to P on the other. These results are at sharp variance from those of Sharma reported earlier (1985). Sample period used in this study is from 1954 to Both annual as well as quarterly data have been used for the estimation of the parameters of regression equations. The analysis based on annual data did not help him to get clear results because of limited degrees of freedom. He therefore concentrated on the quarterly data. Biswas and Saunders (1990) have examined the relationship between the money supply and the price level in India, using quarterly data for two periods and This study relies on the optimal lag selection causality technique outlined by Hsaio (1981). The results obtained by Hsiao s causality testing method are compared with the results of a similar study reported by Sharma (1985). The studies attempt to establish a causal flow between the stock of money and nominal income. The study revealed that the lag selection in causality testing has a critical effect on the test result. An arbitrary lag selection determines the outcome of the tests. Consequently, it becomes crucially important to select an appropriate lag structure in causality testing. The minimum Final Prediction Error (FPE) causality testing technique used throughout the present study overcomes some of the difficulties associated with arbitrary lag selection methods. In particular, under the minimum FPE procedure the causality test results are no longer influenced by an arbitrary lag selection. When the minimum FPE method is applied to Indian data, feedback is established between all the three measures of the money supply and wholesale prices for both test periods to 1980 and 1957 to When M 1 is taken as the measure of the money supply, the results contradict Sharma s (1985) findings. Furthermore, the induction of a relatively high inflationary period does not affect the basic causality relationships under investigation. The results reported in this study are consistent with the situation where budget deficits are financed by creation of money, as is the case of India. Thacker Nita (1992) examined the relationship between money and prices and tried to determine whether inflation in India is caused by monetary growth has been investigated by several authors. Thacker used monthly data on M 1, M 2, CPI and WPI for the period January 1962 to October She also used the statistical theory of integrated regression to establish the time series properties of M 1, M 2, CPI and WPI series for India and used these empirical properties of the data to specify and test Granger causality between the different time series. The results indicate that while monetary growth does cause inflation as measured by both the CPI and the WPI, the reverse is not necessarily true, while changes in WPI Granger cause change in both M 1 and M 2, change in CPI does not cause changes in M 1 or M 2. Therefore there is unidirectional causality from M 1 to CPI and M 2 to CPI, however there is bi-directional causality from M 1 to WPI and M 2 to WPI. The causality from money to prices is not difficult to explain given the familiar monetary argument that inflation is bound to occur when there is too much money chasing too few goods. The reverse causality (from WPI to M 1 and WPI to M 2 ) is difficult to explain in term of the established theories. According to her it can be explained in terms of the distribution of political power in India but she has not given any explanation on this argument. Jadhav, Narendra (1994) analysed the issue of causality between money and prices as well as between money and output in the Indian context using the Direct Granger test as well as the modified Sims test. Salient features of this study are as under: (i) The period covered was fairly long i.e. 34 years covering to (ii) Annual data were used to obviate the necessity of deseasonalising the raw database on higher frequencies (i.e. Monthly or Quarterly) (iii) Money stock was measured by the broad money (M 3 ), Output by GDP at prices and the price level by the GDP deflator (iv) Pre-filtering technique was not used. In order to eliminate obvious deterministic and non-stationary elements, simple time trend equations were fitted for the relevant series and corresponding residuals from these regressions were then used in estimating the equations necessary for testing (v) All the required equations were estimated using 6 lags on the dependent variable and 3 lags for the independent explanatory variable. While performing the Modified Sims test, 3 leads (future values) were used. The lag selection was based on residuals being white noise. There is no strong support for the reverse causality, i.e. from prices to money as the F-values corresponding to both tests are not statistically significant even at 10 percent level of significance. Fairly large F-values, however, heuristically indicate that a weak reverse causation in not entirely ruled out. In this case, the proposition that money causes output is supported by both the tests, though somewhat weakly. One the other hand, there is no support for the reverse causation from output to money as suggested by extremely low F-statistic value. 3 Granger Causality: Correlation does not necessarily imply causation in any meaningful sense of that word. The econometric graveyard is full of magnificent correlations, which are simply spurious or meaningless.

3 International Conference on Applied Economics ICOAE 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. 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. Bivariate regressions of the form y t = α ο + α 1 y t α 1 y t -l + β 1 x t β 1 x -l + ε t (1) x t = α ο + α 1 x t α 1 x t -l + β 1 y t β 1 y -l + u t (2) for all possible pairs of (x,y) series in the group. The reported F-statistics are the Wald statistics for the joint hypothesis: β 1 = β 2 = = β l = 0 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. 4. Methodology and data sources In the present paper, we have examined the issues of causality between money supply [both Narrow money (M 1 ) and Broad money (M 3 ) defined by RBI] and price level (measured by WPI) in India taking monthly data. We have used Granger causality test for this purpose. The sample period used in this study is from April 1953 to December There is nothing specific about the April 1953 as the beginning point. It could have been 1951 as well as but since monthly series on M 1, M 3 and WPI recently made available by RBI, we preferred to use the data from April 1953 to December Few observations were lost while creating stationary series by the process of deseasionlising. Thus we preferred to work taking data from June 1953 to December Selection of January 1951 or June 1953 as a starting point is not likely to affect our findings in a significant way. Monthly data have been used for analyzing causality between money supply and inflation. To begin with, we use the ratio-to-moving average-multiplicative method to deseasonilise the series on both sides of regression equation and then we convert them into natural logarithmic form for reducing variations in them. Then five different criteria viz. LR (Sequential modified Likelihood Ratio test statistic (each test at 5% level), FPE (Final Prediction Error), AIC (Akaike Information Criterion), SC (Schwarz Information Criterion) and HQ (Hannan-Quinn Information Criterion) have been used to select the appropriate lag order. For the lag order thus selected, pairwise Granger Causality test has been performed with a pair of Null Hypotheses viz. Money supply does not Granger cause WPI and WPI does not Granger cause Money supply. Depending upon the values of F-statistic and the associated significance levels, appropriate conclusions are drawn. The entire data analysis was done using the software E-views Stationarity of WPI monthly series: April 1953 to December 2005 Table 1.1 gives values of various test statistics used for testing stationarity of the monthly WPI series. As can be seen from this table, the ADF test statistics in level (logarithmic) shows presence of unit root in WPI (level). Here intercept coefficient alone as well as intercept and trend coefficient taken together are statistically significant but ADF is not significant. Therefore, it may be concluded that the series is not stationary. For the WPI (first difference) series it is found that ADF statistics is statistically significant indicating absence of unit root. In this case both intercept coefficients alone is statistically significant but trend coefficient is not statistically significant. When the WPI (second difference) series is considered one finds that there is absence of unit root but both criteria AIC and SBC are not minimized. Moreover, intercept coefficient is not statistically significant suggests that WPI (first difference) with intercept series is found to be stationary. These results are depicted graphically in Graph 1.1 and Stationarity of Narrow Money (M 1 ) and Broad Money (M 3 ) monthly series: April 1953 to December 2005 Both M 1 and M 3 series, become stationary when second differences are considered without including intercept and intercept with trend. The relevant results of the test statistics are provided in Table 1.2 for M 1 and Table 1.3 for M 3. Graphs 1.3 and 1.4 (for M 1 ) and Graphs 1.5 and 1.6 (for M 3 ) represent graphically non stationatiry of first differences and the stationarity of second differences of these series. Hand Book of Monetary Statistics of India (2006), Reserve Bank of India, Mumbai

4 248 International Conference on Applied Economics ICOAE 2008 Graph 1.1 and 1.2 Stationarity of Wholesale Price Index (WPI) Monthly Series from April 1953 to December 2005 (Base = 100) Graph: 1.1 First Difference Graph: 1.2 Second Difference -.25 Table 1.1 Values of various test statistics Wholesale Price Index (WPI) Monthly series from April 1953 to December 2005 (Base: =100) WPI (Level) ADF AIC SBC Remark None Intercept Intercept coefficient statistically significant Intercept with Trend Intercept and trend coefficient statistically significant WPI (First Difference) None * Intercept * Intercept coefficient statistically significant Intercept with Trend * Intercept coefficient is statistically significant but trend coefficient is not statistically significant WPI (Second Difference) None * Intercept * Intercept coefficient is not statistically significant Intercept with Trend * Intercept and trend coefficient is not statistically significant *Significant at 99 percent Result: Wholesale Price Index (WPI) stationary at First Difference with Intercept Graph No 1.3 and 1.4 Stationarity of Narrow Money Supply (M 1 ) Monthly Series from April 1953 to December 2005

5 International Conference on Applied Economics ICOAE Graph: 1.3 First Difference Graph: 1.3 Second Difference Table : 1.2 Values of various test statistics Narrow Money Supply (M 1 ) Monthly series from April 1953 to December 2005 M 1 (Level) ADF AIC SBC Remark None Intercept Intercept coefficient is not statistically significant Intercept with Trend Intercept and trend coefficient statistically significant M 1 (First Difference) None * Intercept * Intercept coefficient statistically significant Intercept with Trend * Intercept and trend coefficient statistically significant M 1 (Second Difference) None * Intercept * Intercept coefficient is not statistically significant Intercept with Trend * Intercept and trend coefficient is not statistically significant *Significant at 99 percent Result: Narrow Money Supply (M 1 ) stationary at Second Difference without Intercept and Intercept with Trend Graph No 1.5 and 1.6 Stationarity of Broad Money Supply (M 3 ) Monthly Series from April 1953 to December 2005

6 250 International Conference on Applied Economics ICOAE 2008 Graph: 1.5 First Difference Graph: 1.6 Second Difference Table: 1.3 Values of various test statistics Broad Money Supply (M 3 ) Monthly series from April 1953 to December 2005 M 3 (Level) ADF AIC SBC Remark None Intercept Intercept coefficient is not statistically significant Intercept with Trend * Intercept and trend coefficient is not statistically significant M 3 (First Difference) None * Intercept * Intercept coefficient statistically significant Intercept with Trend Intercept and trend coefficient statistically significant M 3 (Second Difference) None * Intercept * Intercept coefficient is not statistically significant Intercept with Trend * Intercept and trend coefficient is not statistically significant *Significant at 99 percent Result: Broad Money Supply (M 3 ) stationary at Second Difference without Intercept and Intercept with Trend Case 1: Causality of M 3 and WPI (Monthly Data): The values and their significance level of various criteria for selecting appropriate lag order are provided in Table 1.4 below

7 International Conference on Applied Economics ICOAE Table 1.4 VAR Lag Order Selection Criteria Lag LogL LR FPE AIC SC HQ NA 1.96E E E E E E E E E E E E * * * 8.06E-09* * E E E E E E E E E E E E E E E E E E E E E E E E * indicates lag order selected by the criterion It can be seen from this table that 3 out of 5 criteria (viz. LR, FPE, AIC) indicate selection of lag order 12 and SC and HQ criteria suggest selection lag order 11. Table Pairwise Granger Causality Tests Lags: 11 Null Hypothesis: Obs F-Statistic Probability M 3 does not Granger Cause WPI WPI does not Granger Cause M M 3 (Monthly) stationary at Second Difference without Intercept and Intercept with Trend WPI (Monthly) stationary at First Difference with Intercept

8 252 International Conference on Applied Economics ICOAE 2008 Table Lags: 12 Pairwise Granger Causality Tests Null Hypothesis: Obs F-Statistic Probability M3 does not Granger Cause WPI WPI does not Granger Cause M M 3 (Monthly) stationary at Second Difference without Intercept and Intercept with Trend WPI (Monthly) stationary at First Difference with Intercept The results of Granger Causality test are given in Table and according to which there is unidirectional causality running from WPI to M 3 for both set of criteria. Case 2: Causality of M 1 and WPI (Monthly Data) A similar exercise was repeated by substituting M 1 for M 3. Results of various test statistics are given in Table 1.5 Table 1.5 VAR Lag Order Selection Criteria Lag LogL LR FPE AIC SC HQ NA 8.42E E E E E E * * E E E E E E E E E E E E-08* * E E E E E E E E E * 3.97E E E E E E E E E E * indicates lag order selected by the criterion In this case the lag order selected based on SC and HQ is 5, while the lag order 0f 17 has been selected on the basis of FPE and AIC criteria. Also the LR criterion suggests selection of lag order 27.

9 International Conference on Applied Economics ICOAE Table Pairwise Granger Causality Tests Lags: 5 Null Hypothesis: Obs F-Statistic Probability M 1 does not Granger Cause WPI WPI does not Granger Cause M M 1 (Monthly) stationary at Second Difference without Intercept and Intercept with Trend WPI (Monthly) stationary at First Difference with Intercept Table Pairwise Granger Causality Tests Lags: 17 Null Hypothesis: Obs F-Statistic Probability M 1 does not Granger Cause WPI WPI does not Granger Cause M M 1 (Monthly) stationary at Second Difference without Intercept and Intercept with Trend WPI (Monthly) stationary at First Difference with Intercept Table Pairwise Granger Causality Tests Lags: 27 Null Hypothesis: Obs F-Statistic Probability M 1 does not Granger Cause WPI WPI does not Granger Cause M M 1 (Monthly) stationary at Second Difference without Intercept and Intercept with Trend WPI (Monthly) stationary at First Difference with Intercept The Granger Causality test based on the selection of above mentioned lag orders indicate that lag order 5 does not suggest any causality unidirectional or bidirectional. However, the lag 17 weakly indicates unidirectional Granger causality running from WPI to M 1. Similarly the lag order 27 also indicates a unidirectional Granger causality running from WPI to M 1. Thus one may conclude that WPI Granger causes M 1 and M 3, though in the case M 1 strength of causality is slightly weak. Comparing our results with those obtained by some earlier researchers, we found that (1) Ramchandra (1983) using yearly data from 1951 to 1871 and applying Sim s test found that Money caused price level. (2) In their study, Nachne and Nadkarni (1985) concluded that there was uniformity among all the test results they used (viz. Sim s, FPE, Cross correlation test and transfer function test) in suggesting a unidirectional causality from money stock to price. (3) Ramlal Sharma (1987) observed that there exists a unidirectional causality running from M 1 to P and also from M 3 to P. However, his results are at sharp variance from those obtained by him earlier [Sharma 1985] (4) The study by Biswas and Saundars (1990) using annual data for and concluded that the minimum FPE method suggest a reveres causation i.e. from WPI to M 1 or M 3 contradicting Sharma s (1985) findings. (5) Nita Thacker (1992) in her study based on monthly data from January 1962 to October 1990 came to the conclusion that there was bidirectional causality from M 1 to WPI and M 2 to WPI. (6) Jadhav Narandra (1994) had found that on the basis of fairly large F values that there was a possibility of a weak reverse causation (i.e. from WPI to Money supply) it should be noted here that he used annual data of 34 years from to Thus a majority of studies (including ours) found existence of reverse causality i.e from Price to Money supply. 5. Conclusions: Thus our main conclusions may be summarized as follows: 1) Money is endogenous as it is partially determined by the price level 2) Selection of definition of money supply does not affect the direction of causality between WPI and money supply as the results are similar for M 1 and M 3. 3) Monetary policy especially regarding money supply has limited implication for controlling inflation in India. 4) Increasing money supply in India is mainly due to increase in the rate of inflation.

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