Price hikes, economic reforms and causality in money, income and prices: evidence from Pakistan.
Article Type: Report
Subject: Economic reform (Research)
Economic conditions (Research)
Pricing (Research)
Economic indicators (Research)
Authors: Husain, Fazal
Rashid, Abdul
Pub Date: 06/22/2009
Publication: Name: Pakistan Development Review Publisher: Pakistan Institute of Development Economics Audience: Academic Format: Magazine/Journal Subject: Business, international; Social sciences Copyright: COPYRIGHT 2009 Reproduced with permission of the Publications Division, Pakistan Institute of Development Economies, Islamabad, Pakistan. ISSN: 0030-9729
Issue: Date: Summer, 2009 Source Volume: 48 Source Issue: 2
Topic: Event Code: 310 Science & research Computer Subject: Product price
Product: Product Code: 9914170 Pricing Policy
Geographic: Geographic Scope: Pakistan Geographic Code: 9PAKI Pakistan
Accession Number: 242017202
Full Text: This study extends the analysis of causality by Husain and Rashid (2008) by examining the shift in the variables due to the price hikes in Pakistan in the early 1970s. We investigate the causal relations between real money and real income, between nominal money and nominal income, and between nominal money and prices using the annual data set from 1959-60 to 2003-04. Moreover, we examine the stochastic properties of the variables used in the analysis, and take care of the shifts in the series due to price hikes and liberalisation measures through dummy variables. The results indicate significant shifts in the variables during the sample period. In this context, the shift that occurred due to price hikes in the early 1970s seems to be more important to be incorporated in the analysis. The study finds the active role of money as the leading variable in changing prices without any feedback. In the earlier studies on income the feedback mechanism of money is found missing perhaps because of overlooking the shift in the macro economic variables in the early 1970s.

JEL classification: E3, E4, N3

Keywords: Money, Income, Prices, Price Hikes, Causal Relations, Pakistan

I. INTRODUCTION

Money, Income, and Prices are important macroeconomic variables which play a crucial role in an economy. In this context, the role of money in the determination of income and prices has long been debated particularly between the Keynesians and the Monetarists who hold opposite views in this regard. The Monetarists claim 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 stocks. That is, the direction of causation runs from money to income and prices without any feedback. The Keynesians, on the contrary, argue that money does not play an active role in changing income and prices. In fact income plays the leading role in changing money stocks via demand for money implying that the direction of causation runs from income to money without any feedback. Similarly, changes in prices are mainly caused by structural factors.

The empirical evidence in this regard also remains inconclusive. For example, Sims (1972) examining the causal relationship between money and income in the US economy found the evidence of a uni-directional causality from money to income. Similarly, Brillembourg and Khan (1979) using a longer data set found a unidirectional causality from money to income and prices in the U.S. as claimed by the Monetarists. However, the other studies on the issue reported opposite or different results. For example, Williams, Goodhart, and Gowland (1976) found unidirectional causality from income to money in the UK economy as suggested by the Keynesians. On the other hand, Barth and Bennett (1974), and Lee and Li (1983), found the evidence of a bi-directional causality between income and money in the economies of Canada and Singapore. However, regarding money-prices causality, the evidence seems to be consistent as the results of Williams, Goodhart, and Gowland (1976) and Lee and Li (1983) are similar to that of Brillembourg and Khan (1979); that is, a unidirectional causality from money to prices.

In Pakistan too the issue has long been investigated but with different results. For example, Khan and Siddiqui (1990) found uni-directional causality from income to money and bi-directional between money and prices. On the other hand, Bengali, Khan, and Sadaqat (1999) found a bi-directional causality between money and income and uni-directional from money to prices. Abbas (1991) also found bidirectional causality between money and income in Pakistan while performing the causality test in Asian countries. Jones and Khilji (1988) while analysing causal relationship between money and prices in Pakistan found the evidence of a bidirectional causality with money supply leading. But Siddiqui (1989) found bidirectional causality between the two with prices leading. Finally, Husain and Rashid (2008) in a comprehensive investigation on the issue which covers a longer data set, uses both the real and nominal terms of money and income, and mindful of the shifts in the series due to the economic liberalisation programme, found the evidence of a unidirectional causality from income to money and from money to prices. The study does not find the role of money in increasing national income even after taking care of liberalisation measures.

This study, extending Husain and Rashid (2008), attempts an investigation of the causal relationship between money and income and between money and prices while being cognisant of another important shift in Pakistan's economic data. The price hikes in the early 1970s, generally termed as Oil Price Shocks, had significant impact not only on the economy of Pakistan but also on the economies the world over. We investigate the causal relations between real money and real income, between nominal money and nominal income, and between nominal money and prices using the data set from 1959-60 to 2003-04 with due regard to the stochastic properties of the variables used in the analysis. In addition, we take note of the two shifts, that is, the shifts due to price hikes as well as due to economic reforms together.

The rest of the paper is organised as follows. The next section discusses the data and outlines the methodology to test the stochastic properties of the variables and their interrelationship. Section III presents the descriptive statistics regarding money, income, and prices as well as the stochastic properties of these variables. Sections IV, V, and VI examine causal relations between real money and real income, nominal money and nominal income, and nominal money and prices respectively. The final section contains the summary and conclusions.

II. DATA AND METHODOLOGY

We use annual data from 1959-60 to 2003-04 to investigate the causal relations of money with income and prices in Pakistan. The Gross National Product (GNP) at current prices and constant prices of 1980-81 are used as nominal and real income, broad measure of money (M2) and GDP Deflator with base 1980-81 are used as Money and Prices, respectively. Finally, real money is obtained by deflating M2. The principal data source is the National Accounts of Pakistan, prepared by the Federal Bureau of Statistics. The other data sources include Economic Surveys by the Finance Division and Annual Reports by the State Bank of Pakistan.

We start by presenting the descriptive statistics that show the basic characteristics of the variables used in the analysis. The formal investigation, however, starts with examining the stochastic properties of the variables used in the analysis. Hence, the Unit Root Test is performed on the variables to test the stationarity of the variables. In this context, the widely used Augmented Dickey Fuller (ADF) test is used. We also use Phillips-Perron (PP) tests which is robust to a wide variety of serial correlation and heteroskedasticity, where the truncation lag parameters are determined following Schwert's (1987). Next, we apply the Engle-Granger Co-integration test to explore the long run relations among the variables. Finally, the causal relationships between these variables are examined through Granger causality and/or Error Correction Models (ECM). In all cases lag lengths are decided on the basis of minimum Final Prediction Error (FPE) and Akaike Information Criteria (AIC).

The sample period, 1959-60 to 2003-04, has been subjected to various changes due to economic and political events. In this context an important event that is likely to significantly affect the variables used in the analysis is the economic liberalisation programme started in the early 1990s. Husain and Rashid (2008) taking note of the event did not find any significant change in the role of money in the causality analysis. We extend their analysis by referring to another event that significantly affected the macro variables in Pakistan in the early 1970s i.e., the price hikes that in fact affected the economy significantly around the world. Moreover, we take note of the two events together. Hence we include a dummy from 1972-73 onwards to reflect the effects of price hikes and a dummy from 1991-92 onwards to refer to the economic reforms.

III. MONEY, INCOME, AND PRICES IN PAKISTAN

We start, following Husain and Rashid (2008), by presenting the descriptive statistics of the variables used in the analysis for both the full sample and the two subsamples. Though the number of observations in sub-sample I is very low relative to the observations in sub-sample II, it can provide some insights regarding the percentage changes in the variables. The results are shown in Table 1.

The table shows an average annual expansion of around 13 percent in nominal money. With an expansion of around 7 percent in prices, the real money has expanded by 6 percent. Similarly, nominal and real incomes have increased over time with an expansion of 12.6 percent and 5.4 percent respectively. The table further shows the descriptive statistics for the two sub-samples. Sub-sample I covers the period before the price hikes, whereas sub-sample II represents the periods after the price hikes. Moreover, we also conduct the tests for equality of means and variances between the two sub-samples, The results indicate significant increase in the means of the two nominal variables along with prices. In fact, the average expansion in prices has increased by three times in the second sample. The table also indicates significant increase in variations in real money as well as in the two nominal variables.

The formal investigation is done through Co-integration and Error Correction Model framework. At the first step, the variables are tested for the unit roots by applying both the ADF and PP tests. The results are reported in Table 2.

The table indicates that the variables are, in general, first differenced stationary, i.e., I(1). We now proceed to investigate the causal relation between the two variables by estimating the co-integrating regression suggested by Engle-Granger. If co-integration is found, the Error Correction Models are estimated. Otherwise, the Granger causality equations are estimated. In all cases the lag lengths are decided on the basis of Log Likelihood, Akaike and Bayesian information criteria. The next three sections investigate the causal relations between real money and real income, nominal money and nominal income, and nominal money and prices.

IV. CAUSALITY BETWEEN REAL MONEY AND REAL INCOME

We start by looking at the causal relation between the two real variables, real money and real income. In this context, we reproduced the results reported by Husain and Rashid (2008) indicating no short run and long run causal relations between the two variables. Table 3(a) shows the results.

They, however, found significant impact on the relations between the two variables following the shift due to the start of the economic reforms in the 1990s. In this study we consider another important shift during the sample period, the shifts due to price hikes in the early 1970s.

Shifts in Real Money and Real Income Due to Price Hikes

To reflect the shifts in real variables due to the price hikes we introduce a dummy variable in the analysis that takes the value of one from 1972-73 onwards. The results are reported in Table 3(b).

The dummy variable in the co-integrating regression is highly significant indicating significant shift in the relation between real variables. The ADF and PP tests are also highly significant indicating the existence of a strong relation between real money and real income in the long run. The error term in money equation is also highly significant and verifies the strong long run relation between real variables. Finally, the analysis indicates a uni-directional causality from real income to real money in the long run. In the short run the real variables do not seem to affect each other.

Shifts in Real Money and Real Income Due to Both Prices and Reforms

To incorporate both the shifts, price hikes and economic reforms, we include another dummy, D2, which takes the value of one from 1991-92 onwards in addition to the dummy for prices, D1. The results are shown in Table 3(c).

Both the dummies are significant where the dummy for prices has a greater magnitude. The results remain the same as in the case where only the dummy for prices is included, that is, a uni-directional causality from real income to real money in the long run with no short run causality.

V. CAUSALITY BETWEEN NOMINAL MONEY AND NOMINAL INCOME

We now turn to examine the causal relation between the two nominal variables using the same procedure adopted earlier. The first set of results is shown in Table 4(a).

The PP tests in Co-integrating regression are insignificant rejecting any long run relation between the two nominal variables. However, the ADF test is significant at 10 percent level of significance. Hence, we can say that there is a weak evidence of any long run relation between the two nominal variables. The Error Correction equations verify the weak long run relation where the error term is significant at 10 percent in the money equation. The equations indicate a weak evidence of unidirectional causality from nominal income to nominal money in the long run with no short run causal effects. If we assume no Co-integration, the Granger equations show the evidence of income affecting money at 2nd lag, although the F-test is not statistically significant.

Following Husain and Rashid (2008) we report the analysis for the 3rd lag too. The results show that the error term in error correction equations has become insignificant implying no long run relation between money and income. The equations further show the significant effects of income on money at 3rd lag verified by F-value. The same result is shown by Granger equations if we ignore the error term.

Hence, there is evidence of a one-way causation from nominal income to nominal money although the existence of a long run relation between the two nominal variables is not clear. There is also persistent evidence of nominal income affected by its own first lag and affecting money at three years' lag. We now proceed to take note of the shifts in nominal variables during the sample period.

Shifts in Nominal Money and Nominal Income Due to Price Hikes

The results reflecting the shifts in nominal variables due to the price hikes in the early 1970s are reported in Table 4(b) which shows that as in the case of real variables the shift in the relation of nominal variables due to the price hikes is very significant. Once again, the ADF and PP tests have become highly significant indicating strong evidence of a long run relation between the two nominal variables. However, the most significant change occurs in the direction of causality. Now the results show the bidirectional causality between nominal money and nominal income in the long run. In the short run, however, no causal relation between the two still prevails. Following the procedure adopted previously, we do the analysis for the third lag that also indicates significant change. The persistent three years lag effect of income on money now disappears.

Hence by incorporating the shifts in the nominal variables due to the price hikes we have found the feedback mechanism of money in changing income in Pakistan. It may be mentioned here that Husain and Rashid (2008) in a similar kind of analysis that took note of the shift due to the economic reforms did not find such mechanism of money. We now consider both the shifts, the shifts due to the price hikes and the economic reforms, together to further explore the issue.

Shifts in Nominal Money and Nominal Income Due to Both Reforms and Prices

The results reflecting both the shifts are reported in Table 4(c).

The table shows that, as in the case of real variables, the two dummies are significant and the dummy for prices has greater magnitude. It can be seen that the result regarding causality is similar to the one when only one dummy, the dummy for prices, is included.

Hence, the analysis indicates, as in the case of real variables, significant shifts in the nominal variables during the sample period. Similarly, the shift that occurred in the early 1970s due to price hikes seems to be very crucial to be incorporated in the analysis as it significantly changes the results. The results indicate the existence of a long run relation between nominal money and nominal income where the two variables seem to affect each other in the long run. In the short run, however, the two nominal variables, as the real variables, appear to be independent of each other.

VI. CAUSALITY BETWEEN NOMINAL MONEY AND PRICES

Finally, we investigate the causal relation between nominal money and prices using the same procedure adopted in the previous sections. The first set of results is reported in Table 5(a).

Both the ADF and PP tests are highly significant indicating the existence of a long run relation between money and prices in Pakistan. The error correction equations suggest a uni-directional causality from money to prices in the long run. However, in the short run there is evidence of prices affecting money at 2nd lag although F-value is not significant. Once again as in the case of nominal income we do the analysis for the 3rd lag. However, the result (not reported here) shows no significant lags in either equation indicating no short run causal effects. However, it verifies the uni-directional causality from money to prices in the long run.

Shifts in Money and Prices Due to Price Hikes

The results reflecting the shifts in the two variables due to the price hikes in the early 1970s are reported in Table 5(b).

The dummy variable in the co-integrating regression shows signs of a significant shift in the relationship of money to prices. However, the results remain the same, that is, a unidirectional causality from money to prices in the long run with indication of prices affecting money at two years' lag.

Shifts in Money and Prices Due to Both Reforms and Prices

Finally, the results reflecting both the shifts are reported in Table 5(c).

The table shows that the shift in the money-price relationship is significant in the case of price hikes but not in the case of reforms. Once again, the results have not changed. Hence, there is persistent evidence of a uni-directional causality from money to prices in the long run.

Hence, we can say that the relationship between money and prices in Pakistan does not seem to be affected by the shifts in the variables during the sample period. However, the shift that occurred in the early 1970s due to price hikes seems to be greater in this case too. The results indicate the existence of a long run relation between money and prices where money seems to lead prices in the long run. In the short run there is some indication, though not significant, of prices affecting money with two years' lag. There is also persistent evidence of prices affected by their own first lag.

VI. SUMMARY AND CONCLUSIONS

The objective of this study is to extend the analysis of causality by Husain and Rashid (2008) by taking cognizance of the shift in the variables due to the price hikes in the early 1970s. Following them we investigate the causal relations between real money and real income, between nominal money and nominal income, and between nominal money and prices using the annual data set from 1959-60 to 2003-04, examining the stochastic properties of the variables used in the analysis, and in consideration of the expected shifts in the series through dummy/ies.

The formal analysis indicates significant shifts in the variables during the sample period. These shifts include the price hikes in early 1970s and the start of the economic reforms in early 1990s. In this context, the shift occurred in the early 1970s seems to be more important to be incorporated in the analysis. In particular, it seems to be very crucial in the case of nominal variables as it has significantly changed the results.

The analysis further indicates the existence of a long run relationship between real money and real income provided that shifts in these variables are given consideration. Moreover, real income seems to be the leading variable that affects real money in the long run. In the short run, the two real variables appear to be independent of each other. Similarly, when money and income are expressed in nominal terms, there is evidence of a one-way causation from income to money although the existence of a long run relationship between them is not clear.

However, the relationship between the two nominal variables is significantly affected by the shift due to the price hikes in the early 1970s. Taking note of the shift indicates the existence of a strong long run relation as well as bi-directional causality between nominal money and nominal income. The results do not change if we also include the shift representing reforms. In the short run, however, the two nominal variables, like real variables, appear to be independent of each other.

As regards the money-price relationship in Pakistan, the analysis shows a long run relation between the two where money seems to lead prices in the long run. In the short run there is some indication, though not significant, of prices affecting money with two years lag. These findings regarding money-price relationship are not affected by the shifts during the sample period.

Finally it can be said that the study finds an active role of money in the Pakistani economy as it is found to be the leading variable in changing prices without any feedback. In the case of income, the study finds the feedback mechanism of money generally missing in earlier studies which may be because of not taking note of the shift in the macro economic variables in Pakistan in the early 1970s.

Limitations of the Study

At the end we would like to point out the limitations which can be considered in future research. This study mainly follows Husain and Rashid (2008) and uses the same sample period, data sources and methodology, except that it examines the impact of a different shift, that is, the shift due to price hikes. Both studies confine to the Bi-variate Causal analysis. However, the extension of the analysis to Multi-variate causal analysis may provide better insights regarding the role of these variables. Similarly, the two studies are based on annual data covering the period from 1959-60 to 2003-4. As mentioned above, various economic and political events have occurred with a significant impact on the macro economic variables. One of the significant event was the separation of the Eastern wing of the country in 1971 that may cause significant effects on macroeconomic variables. Therefore, the use of quarterly data covering the last two decades should be a better option.

On the technical side, the use of recent unit root tests taking care of structural break in the series would be better than the conventional tests used in the study. Similarly, the studies have used the Ordinary Least Square (OLS) approach to test for money, output and prices causality. However, OLS may not be useful in the presence of conditional heteroskedastic errors. In this context, the use of Maximum Likelihood (ML) technique may be better because this technique has a better power to detect causality.

REFERENCES

Abbas, Kalbe (1991) Causality Test between Money and Income: A Case Study of Selected Developing Asian Countries (1960-988). The Pakistan Development Review 30:4, 919-929.

Barth, J. and J. Bannett (1974) The Role of Money in the Canadian Economy: An Empirical Test. Canadian Journal of Economics May, 306-311.

Bengali, K., A. Khan, and M. Saddaqat (1999) Money, Income, Prices and Causality: The Pakistani Experience. Journal of Developing Areas 33: Summer, 503-514.

Brillembourg, A. and M. Khan (1979) The Relationship between Money, Income, and Prices: Has Money Mattered Historically? Journal of Money, Credit, and Banking August, 358-365.

Dereyes, F., D. Starleaf, and G. Wang (1980) Test of Direction of Causation between Money and Income in Six Countries. Southern Economic Journal October, 477-487.

Husain, F. and Abdul Rashid (2002-2008) Economic Liberalisation and the Causal Relations Among Money, Income, and Prices: The Case of Pakistan. Pakistan Journal of Applied Economics 18, 103-121.

Jones, J. D. and N. Khilji (1988) Money Growth, Inflation, and Causality: Empirical Evidence for Pakistan (1973-85). The Pakistan Development Review 27:1, 45-88.

Khan, A. and A. Siddiqui (1990) Money, Prices and Economic Activity in Pakistan: A Test of Causal Relation. Pakistan Economic and Social Review Winter, 121-136.

Lee, S. and W. Li (1983) Money, Income, and Prices and their Lead-lag Relationship in Singapore. Singapore Economic Review April, 73-87.

Pakistan, Government of (1998) 50 Years of Pakistan in Statistics. Islamabad: Federal Bureau of Statistics.

Pakistan, Government of (Various Issues) Economic Survey. Ministry of Finance.

Pakistan, Government of (Various Issues) Monthly Statistical Bulletin. State Bank of Pakistan.

Phillips, R., and P. Perron (1988) Testing for a Unit Root in Time Series Regression. Biometrika June, 335-346.

Schwert, W. (1987) Effects of Model Specification on Tests for Unit Roots in Macroeconomic Data. Journal of Monetary Economics July, 73-103.

Siddiqui, Anjum (1989) The Causal Relation Between Money and Inflation in a Developing Economy. International Economic Journal (Summer), 79-95.

Sims, C. (1972) Money, Income, and Causality. American Economic Review September, 540-552.

Williams, W., C. Goodhart, and D. Gowland (1976) Money, Income, and Causality: The U.K. Experience. American Economic Review June, 417-423

Authors' Note: The authors are grateful to the referees for their useful comments.

Fazal Husain is Chief of Research at the Pakistan Institute of Development Economics, Islamabad. Abdul Rashid is associated with International Islamic University, Islamabad.
Table 1
Descriptive Statistics for Growth in Money, Income, and Prices

                   Real     Nominal    Real     Nominal
                   Money     Money    Income    Income    Prices

                             Full Sample: (1960-61- 2003-04)

Mean              0.0605    0.1325    0.0540    0.1262    0.0720
Std. Dev.         0.0697    0.0541    0.0242    0.0491    0.0499
Observations        44        44        44        44        44

                              Sample I: (1960-61-1971-72)

Mean              0.0728    0.1010    0.0646    0.0910    0.0282
Std. Dev.         0.0434    0.0385    0.0263    0.0326    0.0332
Observations        12        12        12        12        12

                             Sample II: (1972-73-2003-04)

Mean              0.0559    0.1444    0.0500    0.1394    0.0885
Std. Dev.         0.0774    0.0548    0.0225    0.0481    0.0451
Observations        32        32        32        32        32

                            Equality of Means and Variances

Mean (t-value)     0.91     2.94 **    1.70     3.81 **   4.83 **
Variance (F-value)3.18 **   2.02 *     1.36     2.17 **    1.84

Note: ***, **, and * represent significance at 1 percent, 5 percent
and 10 percent.

Table 2
Unit Root Tests for Money, Income, and Prices

                         Levels              First Difference

                 W/O Trend     W. Trend    W/O Trend     W. Trend

ADF

Real Money        -0.49        -3.303       -4.957 **    -4.365 **
Real Income       -2.837       -1.006       -6.119 **    -6.666 **
Nominal Money      0.314       -3.507       -5.012 **    -4.488 **
Nominal Income    -0.399       -1.455       -3.661 **    -3.711 **
Prices             0.089       -2.563       -3.548 **    -3.558 **

PP (W/O Trend)     (1=3)        (1=9)        (1=3)        (1=9)

Real Money        -0.214       -0.103       -4.886 **    -4.763 **
Real Income       -3.104 **    -2.930 **    -6.211 **    -6.745 **
Nominal Money      0.844        1.021       -5.014 **    -4.888 **
Nominal Income    -0.151       -0.162       -3.612 **    -3.540 **
Prices             0.487        0.469       -3.489 **    -3.309 **

PP (W. Trend)      (1=3)        (1=9)        (l=3)        (1=9)

Real Money        -2.54        -2.152       -4.823 **    -4.682 **
Real Income       -0.457       -0.556       -7.325 **    -7.290 **
Nominal Money     -2.6         -2.433       -5.006 **    -4.852 **
Nominal Income    -1.788       -1.992       -3.553 *     -3.457 *
Prices            -2.779       -2.727       -3.488 *     -3.295 *

Note: ** and * represent significance at 5 percent and 10 percent.

Table 3(a)
Causality between Real Money and Real Income

                          Cointegration (Engle-Granger)

                Const.      Coeff.       ADF    PP(1=3)   PP(1=9)

RM on RY     -1.345 ***   1.035 ***    -1.092    -1.387   -1.358

Conclusion: No Co-integration

         Granger Causality

Lag 1         DRY       DRM

DRY(-1)      -0.032    -0.115
DRM(-1)       0.059     0.270
F-value       0.917     0.055

         Granger Causality

Lag 3         DRY       DRM

DRY(-1)      -0.132    -0.348
DRY(-2)       0.267    -0.731
DRY(-3)       0.321     0.729
DRM(-1)       0.086     0.394 *
DRM(-2)      -0.012    -0.089
DRM(-3)      -0.916    -0.117
F-value       1.313     1.328

Conclusion: No Short run Causality upto three lags

Note: ***, **, and * represent significance at t percent, 5 percent
and 10 percent.

Table 3(b)
Causality between Real Money and Real Income (Prices)

                     Cointegration (Engle-Granger)

             Const.         D         Coeff.        ADF

RM on RY   -3.863 ***   -0.428 ***   1.259 ***   -4.943 ***

           Cointegration (Engle-Granger)

               PP(1=3)      PP(1=9)

RM on RY      -4.864 ***   -4.940 ***

Conclusion:Evidence of Co-integration

Error Correction Causality

Lag 1         DRY          DRM

D           0.059 ***    0.017
e(-1)      -0.008       -0.728 ***
DRY(-1)    -0.081       -0.446
DRM(-1)     0.058        0.369
F-value     0.711        1.474

Error Correction Causality

Lag 3         DRY          DRM

D           0.003         0.009
e(-1)       0.062        -0.752 ***
DRY(-])    -0.180        -0.293
DRY(-2)     0.334        -0.206
DRY(-3)     0.299        -0.334
DRM(-1)     0.087         0.295 *
DRM(-2)    -0.019         0.058
DRY(-3)    -0.093        -0.044
F-value     1.078         0.632

No Short run Causality

Note: and * represent signiticance at 1 percent, 5 percent and
10 percent.

Table 3(c)
Causality between Real Moizey and Real Income (Prices and Reforms)

                        Cointegration (Engle-Granger)

           Const.       D1           D2          Coeff.      ADF

RM on RY   -2.735 ***   -0.353 ***   0.124 ***   1.163 ***   -5.238 ***

           PP(1=3)      PP(1=9)

RM on RY   -5.093 ***   -5.008 ***

Conclusion: Evidence of Strong Co-integration

Error Correction Causality

Lag 2      DRY          DRM

D1          0.003        0.004
D2         -0.026 *      0.005
e(-1)       0.051       -0.929 ***
DRY(-])    -0.338       -0.186
DRY(-2)     0.012        0.171
DRM(-1)     0.099        0.381 **
DRM(-2)     0.022        0.082
F-value     1.137        0.211

Conclusion: Unidirectional Causality from Income to Money in the Long curt

No Short run Causality

Note: ***, **, and * represent significance at 1 percent, 5 percent and 10
percent.

Table 4(a)
Causality between Nominal Money and Nominal Income

Cointegration (Engle-Granger)

           Const.       Coeff.       ADF        PP(1=3)       PP(1=9)

NM on NY   -1.100 ***    1.016 ***   -1.859 *    -1.525        -1.451

Conclusion: Weak Evidence of Co-integration

Error Correction Causality           Granger Causality

Lag 2         DNY          DNM        Lag 2         DNY          DNM

e(-1)      -0.037       -0.201 *
DNY(-1)     0.520 **    -0.311       DNY(-1)     0.495 ***    -0.196
DNY(-2)    -0.012        0.125       DNY(-2)    -0.06          0.401 **
DNM(-1)     0.085        0.208       DNM(-1)     0.11          0.261
DNM(-2)     0.019       -0.017       DNM(-2)    -0.009        -0.052
F-value     0.182        1.061       F-Value     0.371         2.346

Conclusion: Weak Evidence of Unidirectional Causality from Income to
Money

Error Correction Causality           Granger Causality

Lag 3         DNY          DNM        Lag 3         DNY          DNM

e(-1)       0.066       -0.075
DNY(-1)     0.569 **    -0.159       DNY(-1)     0.504 ***    -0.097
DNY(-2)    -0.069       -0.005       DNY(-2)    -0.115         0.097
DNY(-3)     0.209        0.559 **    DNY(-3)     0.150         0.520 **
DNM(-1)     0.020        0.034       DNM(-1)     0.061         0.104
DNM(-2)     0.049        0.017       DNM(-2)     0.019         0.022
DNM(-3)    -0.095       -0.025       DNM(-3)    -0.111        -0.056
F-value     0.148        2.503 *     F-Value     0.288         4.034 **

Conclusion: Unidirectional Causality from Income to Money at 3 Years
Lag

Note: ***, **, and * represent significance at 1 percent, 5 percent
and 10 percent.

Table 4(b)
Causality between Nominal Money and Income (Prices)

                 Cointegration (Engle-Granger)

           Const.       D            Coeff.      ADF

NM on NY   -1.846 ***   -0.393 ***   1.097 ***   -4.631 ***

          Cointegration (Engle-Granger)

               PP(1=3)      PP(1=9)
NM on NY
              -4.479 ***   -4.407 ***

Conclusion: Evidence of Co-integration

         Error Correction Causality

Lag 2        DNY          DNM

D           0.066 **      0.060 *
e(-1)      -0.369 ***    -0.449 ***
DNY(-1)     0.239         0.046
DNY(-2)     0.205         0.018
DNM(-1)    -0.072         0.317 **
DNM(-2)    -0.041        -0.011
F-value     0.230         0.063

         Error Correction Causality

Lag 3       DNY          DNM

D           0.056 *       0.050
E(-1)      -0.474 ***    -0.359 **
DNY(-1)     0.236         0.045
DNY(-2)     0.098        -0.075
DNY(-3)     0.370 *       0.324
DNM(-1)    -0.214         0.191
DNM(-2)     0.000         0.018
DNM(-3)    -0.118        -0.065
F-value     1.082         0.753

Conclusion: Bidirectional Causality between Income and Money in
the Long run No Short run Causality

Note: ***, **, and * represent significance at 1 percent, 5
percent and 10 percent.

Table 4(c)
Causality between Nominal Money and Income (Prices and Reforms)

Cointegration (Engle-Granger)

              Const         D1          D2       Coeff.          ADF

NM on NY   -1.451 ***   -0.321 ***   0.117 **   1.059 ***    -4.597 ***

Cointegration (Engle-Granger)

            PP(1=3)      PP(1=9)

NM on NY   -4.411 ***   -4.226 ***

Conclusion: Strong Evidence Co-integration

Error Correction Causality           Error Correction Causality

Lag 2         DNY       DNM          Lag 3      DNY          DNM

Dl          0.080 ***    0.064 *     Dl          0.064 **     0.054
D2         -0.022       -0.015       D2         -0.012       -0.007
e(-1)      -0.442 ***   -0.549 ***   e(-1)      -0.600 ***   -0.438 **
DNY(-1)     0.186        0.001       DNY(-1)     0.193        0.010
DNY(-2)     0.252       -0.132       DNY(-2)     0.192       -0.157
DNM(-I)    -0.076        0.312 *     DNY(-3)     0.407 **     0.279
DNM(-2)    -0.028       -0.002       DNM(-1)    -0.239        0.201
F-value     0.25         0.209       DNM(-2)     0.003        0.021
                                     DNM(-3)    -0.098       -0.081
                                     F-value     1.352        0.522

Conclusion: Bidirectional Causality between Income and Money in the
Long run No Short run Causality

Note: ***, **, and * represent significance at 1 percent, 5 percent and
10 percent.

Table 5(a)
Causality between Nominal Money and Prices

                        Cointegration (Engle-Granger)

            Const.       Coeff.       ADF         PP(1=3)     PP(1=9)

NM on DF   3.850 ***   1.697 ***   -3.696 ***   -2.687 ***   -2.477 **

Conclusion: Strong Evidence of Co-integration

Error Correction Causality

Lag 2           DDF    DNM

e(-1)     -0.314 ***   -0.071
DDF(-1)    0.589 ***   -0.349
DDF(-2)    0.216        0.496 *
DNM(-1)    0.163        0.167
DNM(-2)    0.003        0.045
F-value    0.898        2.446

Conclusion: Unidirectional from Money to Prices in the Long run

Note: ***, **, and * represent significance at 1 percent, 5 percent
and 10 percent.

Table 5(b)
Causality between Money and Prices (Prices)

           Cointegration (Engle-Granger)

           Const.       D            Coeff.      ADF

NM on DF    3.702 ***   -0.172 **    1.755 ***   -3.915 ***

           Cointegration (Engle-Granger)

NM on DF   PP(1=3)      PP(1=9)

           -2.924 ***   -2.734 ***

Conclusion: Evidence of (Co-integration

Error Correction Causality

Lag 2      DDF          DNM

D           0.097 ***    0.038
e(-1)      -0.462 ***   -0.054
DDF(-1)     0.393 ***   -0.324
DDF(-2)     0.244 *      0.490 *
DNM(-1)    -0.002        0.153
DNM(-2)    -0.046        0.032
F-Value     0.130        2.068

Conclusion: Prices in the Long run

Note: ***, 1 percent, 5 percent and 10 percent.

Table 5(c)
Causality between Money and Prices (Prices and Reforms)

                Cointegration (Engle-Granger)

            Const.          D1         D2      Coeff.        ADF

NM on DF      3.556 ***   -0.220 **    -0.081   1.799 ***   -3.953 ***

            Cointegration (Engle-Granger)

              PP(1=3)      PP(1=9)

NM on DF    -2.993 ***    -2.787 ***

Conclusion: Strong Evidence of Co-integration

Error Correction Causality

Lag 2           DDF          DNM

D1            0.102 ***     0.039
D2           -0.010        -0.008
e(-1)        -0.446 ***    -0.076
DDF(-I)       0.307 **     -0.311
DDF(-2)       0.164         0.452 *
DNM(-1)      -0.039         0.162
DNM(-2)      -0.054         0.032
F-value       0.350         1.777

Conclusion: Unidirectional from Money to Prices in the Long run

Note: ***, **, and * represent significance at 1 percent, 5
percent and 10 percent.
Gale Copyright: Copyright 2009 Gale, Cengage Learning. All rights reserved.