REVISITING MONEY DEMAND IN MALAYSIA: SIMPLE-SUM VERSUS DIVISIA MONETARY AGGREGATES

Chin-Hong Puah, Choi-Meng Leong, Shazali Abu Mansor and Evan LauDepartment of Economics, Faculty of Economics and Business,

Universiti Malaysia Sarawak,94300 Kota Samarahan, Sarawak, Malaysia

ABSTRACT

This paper attempts to determine whether Divisia Monetary Aggregates is a superior monetary instrument in Malaysia where monetary targeting has been replaced by interest rate targeting due to the inadequacy of the monetary aggregates in predicting imperative economic activities. Is there any discernible evidence to suggest that Divisia monetary Aggregates possess significant relationship with the macroeconomics indicators? The empirical results in this study indicate that Divisia M2 performs excellently in the money demand function of Malaysia that incorporated the financial reform and financial development variables. The significance of Divisia money validates the usefulness of monetary targeting to formulate monetary policy in Malaysia.

Keywords: Divisia Money, Money Demand, Error-Correction Model

INTRODUCTIONThe interest on the study on money demand has continued to draw considerable passion among researchers. The posit between monetary policy instruments and macroeconomic variables is well accepted in the field of monetary economics, where the monetary targeting and the interest targeting are the two major intellectual debates in this field. Arguments on the instrument used in monetary policy stemmed from the evolvement of the financial markets globally. There is substantial evidence in the developed countries to suggest that the Divisia monetary aggregate is a superior monetary targeting than interest targeting for these developed nations. Divisia definition of money emerges as simple-sum monetary aggregates are distorted due to the financial reforms Is this phenomenon also true for emerging economies nations particularly Malaysia?

The monetary policy that based on monetary targeting as intermediate policy target is critically depends on the role of money as the transmission mechanism to achieve macroeconomic stability. The traditional monetary aggregates such as M1, M2 and M3 are constructed and employed by the monetary authorities in most of the countries. These monetary aggregates are also named as simple-sum monetary aggregates. However, simple-sum definitions of money become flawed attributable to the financial innovation and liberalization. Simple-sum measures of money fail to maintain a stable relationship with the fundamental macroeconomic indicators such as Gross Domestic Product (GDP) and inflation. As a result, many countries have shifted Corresponding author: E-mail: chpuah@feb.unimas.myAcknowledgement: The authors acknowledge the financial support rendered by Universiti Malaysia Sarawak (UNIMAS) through the Fundamental Research Grant No. FRGS/05(08)650/2007(15).

1

from monetary targeting to other type of policy targets such as inflation targeting and interest rate targeting that can better predict the movements of the important macroeconomic indicators.

Simple-sum monetary aggregates are based on balance sheet or accounting principles and thus it is problematic to utilize them in measuring the monetary services. Yu and Tsui (2000) contend that simple-sum monetary aggregates possess insufficient microeconomic foundation to confirm the assumption of perfect substitution. The reason behind is that financial reforms have formed varieties interest-bearing assets and these newly found assets are included as the components of money. Thornton and Yue (1992) argue that, when more financial assets are included as money, it is inappropriate to assume them as perfect substitutes. Different degree of monetary services provided by each of the asset should be weighted by its ‘moneyness’ in obtaining an appropriate monetary aggregate. For instance, the financial assets with more ‘moneyness’ than the others shall be given larger weights. However, simple-sum monetary aggregates assume that all financial assets are given equal and constant weight of unity over time (Drake and Mills, 2002a; Drake and Mills, 2002b; and Drake and Fleissig, 2004).

In view of the deficiencies of simple-sum monetary aggregates, Barnett (1980) proposes the use of Divisia monetary aggregates to gauge the total monetary services provided by financial assets. Divisia aggregation is consistent with the microeconomic theory and assumes the financial assets as less than perfect substitutes. Financial assets that are more frequently used for transaction purposes have higher opportunity costs and are given higher weights. Conversely, financial assets that are used for saving purposes and incurred fewer transactions have lower opportunity costs and thus are given lower weights.

Binner, Fielding and Mullineux (1999) show that leading indicators which incorporating broader Divisia definition of money (M4) can provide early warning signals for the future movements of inflation. Schunk’s (2001) findings also indicate that broad Divisia monetary aggregates are information content in forecasting the future real economic activity while narrow Divisia monetary aggregate is information content in forecasting future prices when comparing to simple-sum counterparts. In a study by Darrat, Chopin and Lobo (2005), Divisia measures of money ranged from narrow to broad money are found to perform better in capturing the financial innovations and regulatory changes. In addition, Dahalan, Sharma and Sylwester (2005) as well as la Cour (2006) provide evidences that Divisia M2 is superior to simple-sum counterpart in the money demand function.

The significance of Divisia monetary aggregates may shed new light on the possibility for reverting back to monetary targeting with the presence of a stable money demand function. A money demand function that is closely linked to the fundamental macroeconomic indicators plays an important role in verifying the usefulness of monetary targeting.

Monetary Policy in Malaysia

2

Prior the mid-1990s, Bank Negara Malaysia (BNM) - the central bank of Malaysia, adopted monetary targeting in formulating monetary policy. BNM employed M1 for monetary targeting and subsequently replaced it with M3 that is highly correlated with inflation. Nevertheless, rapid evolution in economy and financial system had weakened the effectiveness of M3. The annual growth of M3 became unpredictable during the period of large capital flows in 1992 and 1993. Furthermore, the money demand function became unstable and thus monetary aggregates turned out to be unreliable to serve as policy target. As a result, BNM had shifted the policy target from monetary targeting to interest rate targeting during the mid-1990s by using the Interbank Rate (IBR) and subsequently followed by the introduction of the BNM’s Intervention Rate in 1998 to replace IBR as an interest rate targeting policy indicator (BNM, 1999). The intervention rate was then replaced with the Overnight Policy Rate (OPR) in April 2004.

Malaysia faces inflationary pressure since early 2006 as a result of international crude oil price hike which lead to a raise in the domestic petrol price and also the prices of other petroleum-based products. This caused BNM to increase the OPR several times in short interval in order to control the inflationary pressure. Nevertheless, frequent increases in interest rate do not bode well for growth and businesses in the country as frequent increases lead to uncertainties and also higher cost of borrowings for businesses. Given the significant developments with monetary aggregation theory and inflationary problems faced by the country in recent times, it is now a suitable moment to revisit the possibilities of a return to monetary targeting and determine the usefulness and suitability of Divisia monetary aggregates proposed by Barnett (1980) for the economy of Malaysia and its performance in comparison with the simple sum-money counterparts.

MODEL SPECIFICATION

Divisia Monetary AggregatesDivisia monetary aggregate is constructed based on the microeconomic model of the economic agents’ decision making. Assuming the economic agents try to maximize their utility but subjected to the budget constraints, the total expenditure (Y) on monetary assets at time t (see Anderson, Jones and Nesmith, 1997) will be:

(1)

It is based on the budget constraints, where πit is the user cost of monetary asset i at time t and is the stock of monetary asset i in optimum at time t. The expenditure share on monetary asset i at time t can be expressed as:

(2)

where the total user cost of the optimal monetary aggregates is divided by the total expenditure. The user costs (πit) are the interest rate differentials between the rate of return of a benchmark asset and the own rate of return of a monetary asset. User costs

3

are the opportunity costs of holding monetary assets. The interest rate represents the liquidity of the monetary asset. The user cost or the opportunity cost (Barnett, 1978) can be measured by:

(3)

with Rit is the benchmark rate and rit is the rate of return of an asset. is the consumer price index (CPI). The benchmark rate is the highest rate of return of a risk-free monetary asset that does not provide any monetary services.

Barnett (1980) proposes the use of Divisia quantity index to construct the Divisia monetary aggregate (DM) as follow:

(4)

and the growth rate of Divisia monetary aggregate can be formulated as (see Habibullah, 1999: 80):

(5)

where is the average of the sum of and that can be defined as:

(6)

Money Demand SpecificationSriram (2002) state that the miscellaneous perspective of money demand theories endeavors to focus on the characteristics of money that serve as transactions, speculative, precautionary or utility purposes. Nevertheless, these theories acquire common fundamental indicators to deal with various types of hypotheses. Generally, money demand relates the quantity of money demanded with a faction of vital economic indicators. According to Dickey, Jansen and Thornton (1991), with the assumption that the economic agents do not face money illusion problem, the money demand specification is as follow:

RMd = f (Y, Z) (7)

where RMd is the real money demand and Y is the real income level (real GDP). Z indicates all other variables that affect money demand. In this study, the variables that comprise in Z are interest rate (R), real effective exchange rate (REER) and monetization (MONET). Consequently, the model becomes:

RMd = f(Y, R, REER, MONET) (8)

4

For a standard money demand function, income and interest rate are normally being included. Since Malaysia is a small open economy, exchange rate is included in the money demand function. Marashdeh (1997) and Hueng (1998) take into account the effect of exchange rate on money demand function in the open economy framework. In addition, monetization also incorporated in money demand function. Monetization is the ratio of quasi money (M2 minus M1) to GDP. Since M1 is used to serve as the transaction purposes in the market, subtracting the M1 components from M2 can capture the effect of non-M1 components that serve as the store of value purposes in the market. The non-M1 components are the interest-bearing assets that emerge due to the financial reforms. Hence, monetization can capture the effect of financial reforms since money is being acquired to serve as the store of value purpose. The growth in non-M1 components will increase the demand for money to acquire them and also increase the demand for money to serve as the store of value function of money. Kot (2004) finds that monetization generates positive effect on money demand.

Data DescriptionThis study utilizes quarterly data which ranges from 1981:Q1 to 2005:Q4. Malaysia encounters with financial liberalization at late 1970s and early 1980s. Hence, the starting year of 1981 is significant to identify the monetary aggregate that can capture the liberalization effect. The data series consist of simple-sum monetary aggregate M1 and M2, Divisia monetary aggregate M1 and M2, real GDP, interest rate, real effective exchange rate (REER) and monetization.

The 3-month Treasury bill rate is used as the proxy of interest rate for simple-sum money demand models. Meanwhile, the dual prices for Divisia M1 and M2 are employed as the proxies of interest rates for Divisia money demand models. The REER is the weighted average of all bilateral exchange rates between domestic and major trading partner countries of Malaysia. The monetization can be split into the monetization for simple-sum monetary aggregates and monetization for Divisia monetary aggregates since the money stock is constructed based on different measures of money. All the data series including those used to construct Divisia M1, Divisia M2, M1 dual price and M2 dual price are extracted from various issues of Monthly Statistical Bulletin published by BNM. Simple-sum M1, simple-sum M2, Divisia M1, Divisia M2 and GDP are divided by CPI to obtain the real terms of these variables. All of the data series are transformed into natural logarithms before any test or estimation is conducted.

EMPIRICAL RESULTS

Unit Root Test ResultsThe Augmented Dickey-Fuller test and the Phillips-Perron test are utilized to investigate the time series properties of the data. From Table 1, results imply that all variables under estimation fail to reject the null hypothesis of a unit root in levels. Therefore, all of the variables are non-stationary and contain a unit root. After first differencing, nevertheless, all series become stationary. As all the variables own the same order of integration, which is I(1), we proceed to Johansen and Juselius cointegration test in the next step.

5

------------------------------Insert Table 1 about here------------------------------

Cointegration Test ResultsThe series are regarded as cointegrated when a linear combination of more than one series that is stationary exists among the same series that are non-stationary. The linear combination is the long-run equilibrium relationship of the series and is expressed as a cointegrating equation. If cointegration exists, common stochastic trends are found among a number of variables although each variable has the individual stochastic trend. Johansen (1988) and Johansen and Juselius (1990) have developed the Johansen’s Maximum-Likelihood procedures that comprise of trace and maximal-eigenvalue tests to examine for the cointegrating relationship. The null hypothesis of trace test, which the number of cointegrating vectors is less than or equal to r is tested against a general alternative. On the other hand, null hypothesis that the number of cointegrating vectors is r against a specific alternative hypothesis of r+1 cointegrating vectors is assessed in maximal-eigenvalue test. The results of the trace and maximal-eigenvalue tests are presented in Table 2.

------------------------------Insert Table 2 about here------------------------------

Both the λ-trace and λ-max statistics cannot reject the null hypothesis of no cointegration in simple-sum M1 and Divisia M1 models. One cointegrating vector is found in Divisia M2 model under both tests. Meanwhile, only trace test can identify a cointegrating vector in simple-sum M2 model. According to Cheung and Lai (1993), the skewness and excess kurtosis in innovations of trace statistics are more robust than maximal-eigenvalue statistics when testing the cointegration. Therefore, we consider a single cointegration relationship exists in the simple-sum M2 model. By so doing, simple-sum M2 and Divisia M2 possess a long-run equilibrium relationship with real GDP, interest rate, exchange rate and monetization variables. However, only the model that can generate credible coefficients and carry the correct signs that are consistent with the priori hypothesis of money demand is considered as a well-defined money demand model. Therefore, the coefficients of simple-sum M2 and Divisia M2 real money demand are normalized to one. The coefficients of the variables imply the elasticities of the variables since all of the variables are in logarithms. The restricted cointegration relationships with the implied long-run elasticities of simple-sum M2 and Divisia M2 are illustrated in Table 3.

------------------------------Insert Table 3 about here------------------------------

For simple-sum M2 model, although all of the variables are statistically significant, the coefficient of 3-month Treasury bill rate that exhibits a positive sign is inconsistent with the theoretical concept of money demand. Thus, appropriate money demand function cannot be derived. On the other hand, Divisia M2 model yields excellent results. All variables are statistically significant. Most prominently, the

6

expected signs of the implied long-run elasticities for all variables included are consistent with the priori hypothesis and the sizes for the coefficients are sensible from the economic point of view. The Divisia M2 real money demand function is expressed as follow:

LRDM2 = 1.026LRGDP – 0.254LDUALDM2 – 0.445LREER + 0.953LMONETDM

When there is an increase in income, demand for money will increase since the purchasing power is increased to purchase more goods and services. The relationship of interest rate and money demand is negative. When there is an increase in the interest rate, the demand for the financial assets will be increased and thus reduce the demand for money. Exchange rate and money demand also exhibit negative relationship. Since real effective exchange rate is being utilized, an increase in real effective exchange rate indicates an appreciation in currency. Real effective exchange rate possesses reversed property of nominal exchange rate as nominal exchange rate indicates that an increase in currency value represents depreciation. Therefore, the wealth effect exists in the case of Malaysia, as the appreciation in currency will cause money demand to reduce. When there is an appreciation in Ringgit, Malaysian goods become relatively expensive for the foreigners, especially for the main trading partner countries of Malaysia. As a result, they will demand less for Malaysian products and thus reduce the demand for Ringgit as well. Study by Kararach (2002) also discovers that domestic interest rate and exchange rate have negative impact on the money demand. On the other hand, monetization and money demand exhibit a positive relationship. This implies that the growth of interest-bearing assets will increase the demand for money to acquire them. The result is consistent with the finding of Ahmad (2001) in which the increase in monetization grounds an increase for the demand for money.

Error-Correction Model and Granger-Causality Tests ResultsThe Error-Correction Model (ECM) enables long-run components of variables to obey equilibrium constraints whereas a flexible dynamic specification will be allowed in short-run components. According to Engle and Granger (1987), when there is at least one cointegrating vector of the cointegrated series, the corresponding error-correction representation will exist. As a result, for the cointegrated series, the ECM approach includes the error correction term (ECT) to cope with the short-run adjustment of the cointegrated variables and also transmit short-run correction to shape long-run equilibrium. When applying the ECM approach, the economic model will be transformed to the econometric model. Therefore, Equation 8 is transformed to the following equation:

(9)

where β0 is the intercept and indicates the first difference. As general to specific approach of Hendry and Ericsson (1991) is employed for the short-run dynamic money demand model, the insignificant coefficients will be eliminated sequentially and reparameterized to obtain a parsimonious ECM. β1, β2, β3, β4 and β5 imply the short-run elasticities of lagged value of real money demand, real GDP, interest rate, real effective exchange rate and monetization. The coefficient of ECT (β6) is obtained

7

from the cointegrating vector and therefore contains the long-run information. The results for Granger-causality tests based on ECM are reported in Table 4.

------------------------------Insert Table 4 about here------------------------------

For both simple-sum M2 and Divisia M2 models, the lagged ECTs are statistically significant at 5 percent level. The significance of ECT implies a long-run causal relationship exists between the dependent variable and independent variables in every model. In addition, the significance of ECT enables Wald tests to be performed to examine the short-run Granger-causality of the variables. Empirical results show that, besides the interest rate variable, real GDP, real effective exchange rate and monetization can Granger-cause real money demand in the short-run for both simple-sum and Divisia models. As hypothesized, higher income level will lead to higher demand for money especially to facilitate the transaction needs. Since Malaysia is a small open economy, the exchange rate fluctuations have influential impact on money demand even in the short-run. Monetization variable, which correspond to financial deepening due to the financial reforms, also can affect the real money demand in short time interval.

The speed of adjustment is indicated by the ECT coefficient. ECT coefficient measures the short-run adjustment towards the long-run disequilibrium. From the results, the speed of adjustments to the long-run deviation in the simple-sum M2 model is 15.9 percent. In order to adjust to the long-run deviation, the adjustment will take around 6 quarter periods for simple-sum M2 model. For Divisia M2 model, the speed of adjustments is 26.0 percent. Hence, the short-run deviation will consume around 4 quarter periods to adjust to the long-run equilibrium in Divisia M2 model. Consequently, the speed of adjustment for Divisia M2 is faster than simple-sum M2.

The superiority of Divisia M2 is further supported by the results of diagnostic tests. Diagnostic tests are utilized to assess the accuracy of estimation and the validity of the results derived from the ECM framework. The estimation results are presented in Table 4. Divisia M2 model does not encounter with the problems of normality, serial correlation, heteroscedasticity, mis-specification and parameter instability. However, simple-sum M2 model suffered from serious normality problem. Hence, only Divisia M2 model is stable and passes all diagnostic tests.

The empirical results in this study authenticate the superiority of Divisia M2 monetary aggregate’s performance in the money demand estimation. A correct money demand function is derived and the model is well fitted with a faster speed of adjustment rate. This result is in line with the findings of Dahalan et al. (2005) and la Cour (2006) in which the performance of Divisia M2 is among the best when judged against the other measures of money in the money demand model. Divisia M2 contains important information about the income, interest rate, exchange rate and monetization.

CONCLUSION

8

The broader Divisia monetary aggregate, Divisia M2, is found to be more superior in this study. Divisia M2 model passes all the diagnostic tests and the model is stable as shown by the stability tests. In addition, long-run money demand function can solitarily derived from the Divisia M2 model since the expected signs of the implied long-run elasticities are all consistent with the priori hypothesis and the size for the coefficients is deemed appropriate. The presence of a stable Divisia M2 money demand has reassured the usefulness of monetary aggregate as the indicator for monetary targeting. Monetary targeting has been abandoned by BNM due to the speeding up of financial reforms, which have weakened the relationship between money and important macroeconomic indicators in Malaysia. The significance of Divisia money confirms that monetary aggregate is efficacy as the monetary targeting variable. Monetary targeting can serve as an alternative policy target for BNM as BNM encounters dilemma in adjusting the predetermined interest rate frequently due to inflationary pressure. With the hastening of financial innovations and liberalization, there is intensified potential for monetary targeting to be re-implemented and served as the intermediate policy target for the monetary policy by using the Divisia monetary aggregates.

APPENDIXES

9

TABLE 1: Unit Root Tests Results

Variables

Augmented Dickey-Fuller Test Phillips-Perron Test

Level First Difference Level First DifferenceTrend Without Trend Trend Without Trend

LRSSM1 -3.222 -4.385*** -2.546 -10.046***LRSSM2 -2.035 -3.808*** -1.841 -9.038***LRDM1 -2.330 -3.619** -2.057 -10.917***LRDM2 -2.748 -3.790*** -2.020 -9.583***LRGDP -3.129 -4.963*** -3.192 -9.999***LTBR3 -1.999 -4.078*** -2.195 -8.604***LDUALDM1 -2.066 -5.474*** -2.382 -9.951***LDUALDM2 -2.655 -3.595** -2.557 -10.154***LREER -2.806 -4.098*** -2.551 -6.556***LMONETSSM -2.983 -4.354*** -2.846 -8.867***LMONETDM -2.754 -4.368*** -3.044 -9.992***

Notes: The null hypothesis is the variable contains a unit root. The following notation applies: LRSSM1 and LRSSM2 are natural log of real simple-sum money M1 and M2; LRDM1 and LRDM2 are natural log of real Divisia money M1 and M2; LRGDP is natural log of real GDP; LTBR3 is the natural log of 3-month Treasury bills rate; LDUALDM1 and LDUALDM2 are the natural log of dual prices for Divisia money M1 and M2; LREER is the natural log of real effective exchange rate; LMONETSSM and LMONETDM are the natural log of monetization for simple-sum and Divisia money. Asterisks (**) and (***) denote significant at 5% and 1% levels, respectively.

TABLE 2: Johansen and Juselius Cointegration Tests Results

10

Real Simple-sum M1, Real GDP, 3-Month Treasury Bill Rate, Real Effective Exchange Rate, Monetization (k = 4, r = 0)

H0 H1 λ-trace 95% CV H0 H1 λ-max 95% CVr = 0 r ≥ 1 67.850 69.819 r = 0 r = 1 31.393 33.877r ≤ 1 r ≥ 2 36.457 47.856 r ≤ 1 r = 2 20.626 27.584r ≤ 2 r ≥ 3 15.831 29.797 r ≤ 2 r = 3 10.925 21.132r ≤ 3 r ≥ 4 4.905 15.495 r ≤ 3 r = 4 4.798 14.265r ≤ 4 r = 5 0.107 3.841 r ≤ 4 r = 5 0.107 3.841

Real Simple-sum M2, Real GDP, 3-Month Treasury Bill Rate, Real Effective Exchange Rate, Monetization (k = 4, r = 1)

H0 H1 λ-trace 95% CV H0 H1 λ-max 95% CVr = 0 r ≥ 1 70.995** 69.819 r = 0 r = 1 31.750 33.877r ≤ 1 r ≥ 2 39.245 47.856 r ≤ 1 r = 2 23.015 27.584r ≤ 2 r ≥ 3 16.230 29.797 r ≤ 2 r = 3 11.119 21.132r ≤ 3 r ≥ 4 5.112 15.495 r ≤ 3 r = 4 4.798 14.265r ≤ 4 r = 5 0.314 3.841 r ≤ 4 r = 5 0.314 3.841

Real Divisia M1, Real GDP, M1 Dual Price, Real Effective Exchange Rate, Monetization (k = 4, r = 0)

H0 H1 λ-trace 95% CV H0 H1 λ-max 95% CVr = 0 r ≥ 1 56.636 69.819 r = 0 r = 1 27.645 33.877r ≤ 1 r ≥ 2 28.991 47.856 r ≤ 1 r = 2 14.125 27.584r ≤ 2 r ≥ 3 14.865 29.797 r ≤ 2 r = 3 9.526 21.132r ≤ 3 r ≥ 4 5.339 15.495 r ≤ 3 r = 4 5.284 14.265r ≤ 4 r = 5 0.055 3.841 r ≤ 4 r = 5 0.055 3.841

Real Divisia M2, Real GDP, M2 Dual Price, Real Effective Exchange Rate, Monetization (k = 4, r = 1)

H0 H1 λ-trace 95% CV H0 H1 λ-max 95% CVr = 0 r ≥ 1 77.190** 69.819 r = 0 r = 1 35.320** 33.877r ≤ 1 r ≥ 2 41.869 47.856 r ≤ 1 r = 2 21.747 27.584r ≤ 2 r ≥ 3 20.122 29.797 r ≤ 2 r = 3 13.275 21.132r ≤ 3 r ≥ 4 6.847 15.495 r ≤ 3 r = 4 5.745 14.265r ≤ 4 r = 5 1.102 3.841 r ≤ 4 r = 5 1.102 3.841

Notes: Asterisk (**) denotes significant at 5% level, k is the number of lag and r is the number of cointegrating vector(s).

TABLE 3: Implied Long-Run Elasticities of Normalized Cointegrating Vector

11

ConstantReal Simple-sum M2

Real GDP3-Month Treasury Bill Rate

Real Effective Exchange Rate

Monetization

-2.099 1.000 1.146[22.812]***

0.029[1.909]*

0.328[5.045]***

0.828[9.896]***

ConstantReal Divisia M2

Real GDP M2 Dual PriceReal Effective Exchange Rate

Monetization

2.569 1.000 1.026[31.431]***

-0.254[-6.359]***

-0.445[-3.357]***

0.953[7.827]***

Notes:

Asterisks (*) and (***) indicate significant at 10% and 1% levels, respectively.

TABLE 4: Results of Granger Causality Test Based on ECM

Real Simple-sum M2 Real Divisia M2F-statistics (p-value) F-statistics (p-value)

Real GDP 158.277(0.000)*** Real GDP 8.743(0.000)***

3-Month Treasury Bill Rate

2.473(0.068) Divisia M2 Dual Price

2.769(0.100)

Real Effective Exchange Rate

3.568(0.018)** Real Effective Exchange Rate

5.403(0.002)***

Monetization 124.064(0.000)*** Monetization 4.718(0.011)***

Coefficient [t-statistic] Coefficient [t-statistic]ECT -0.159[-2.545]** ECT -0.260[-2.013]**

Diagnostics Tests: Diagnostics Tests:JB 60.832(0.000)*** JB 2.192(0.334)AR [4] 0.166(0.847) AR [4] 1.129(0.349)ARCH [1] 0.124(0.884) ARCH [1] 0.082(0.775)RESET [1] 0.143(0.966) RESET [1] 3.867(0.053)CUSUM Stable CUSUM Stable CUSUM2 Stable CUSUM2 Stable

Notes: JB is Jarque-Bera normality test of the residuals, AR[4} is a 4 th order Breusch-Godfrey serial correlation Lagrange Multiplier test, ARCH[1] is a 1 st order autoregressive conditional heteroscedasticity test, RESET[1] is a 1st order Ramsey’s RESET test, CUSUM is cumulative sum of recursive residuals stability test and CUSUM2 is cumulative sum of squares of recursive residuals stability test. Asterisks (**) and (***) denote significant at 5% and 1% levels, respectively.

12

REFERENCES

Ahmad, M. 2001. Demand for money in Bangladesh: An econometric investigation into some basic issues. The Indian Economic Journal, 48(1): 84-89.

Anderson, R. G., Jones, B. E., & Nesmith, T. D. 1997. Building new monetary services indexes: Concepts, data and methods. Federal Reserve Bank of St. Louis Review, January/February: 53-82.

Bank Negara Malaysia. 1999. The central bank and the financial system in Malaysia. Kuala Lumpur: Bank Negara Malaysia.

Barnett, W. A. 1978. The user cost of money. Economic Letters, 1: 145-149.Barnett, W. A. 1980. Economic monetary aggregates: An application of index number

and aggregation theory. Journal of Econometrics, 14: 11-48.Binner, J. M., Fielding, A., & Mullineux, A. W. 1999. Divisia money in a composite

leading indicator of inflation. Applied Economics, 3: 1021-1031. Cheung, Y. W., & Lai, K. S. 1993. Finite-sample sizes of Johansen’s likelihood ratio

tests for cointegration. Oxford Bulletin of Economics and Statistics, 55: 313-328.

Dahalan, J., Sharma, S. C., & Sylwester, K. 2005. Divisia monetary aggregates and money demand for Malaysia. Journal of Asian Economics, 15: 1137-1153.

Darrat, A. F., Chopin, M. C., & Lobo, B. J. 2005. Money and macroeconomic performance: Revisiting divisia money. Review of Financial Economics, 14: 93-101.

Dickey, D. A., Jansen, D. W., & Thornton, D. L. 1991. A primer on cointegration with an application to money and income. Federal Reserve Bank of St. Louis Review, March/April: 58-78.

Drake, L. M., & Mills, T. C. 2002a. A new empirically weighted monetary aggregate for the U.S. Loughborough University, Department of Economics, Economic Research Paper, ERP02-12.

Drake, L. M., & Mills, T. C. 2002b. Is M3 an appropriate aggregate for guiding ECB monetary policy? Loughborough University, Department of Economics, Economic Research Paper, ERP02-15.

Drake, L., & Fleissig, A. 2004. Admissible monetary aggregates and UK inflation targeting. Money Macro and Finance (MMF) Research Group Conference 2004, No.2.

Engle, R. E., & Granger, C. W. 1987. Cointegration and error correction: Representation, estimation and testing. Econometrica, 55: 251-276.

Habibullah, M. S. 1999. Rationale for divisia monetary aggregates in ‘deregulated’ Asian developing countries. In M.S. Habibullah (Ed.), Divisia monetary aggregates and economic activities in Asian developing economics: 67-111. Aldershot: Ashgate Publishing Limited.

Hendry, D. F., & Ericsson, N. 1991. An econometric analysis of UK money demand in ‘Monetary Trends in the United States and the United Kingdom by Milton Friedman and Anna J. Schwartz’. American Economic Review, 81: 8-38.

Hueng, C. J. 1998. The demand for money in an open economy: Some evidence for Canada. North American Journal of Economics & Finance, 9(1): 15-31.

Johansen, S. 1988. Statistical analysis of cointegration vectors. Journal of Economic Dynamic and Control, 12: 231-254.

Johansen, S., & Juselius, K. 1990. Maximum likelihood estimation and inference on cointegration - With applications to the demand for money. Oxford Bulletin of Economics and Statistical, 52(2): 169-210.

13

Kararach, G. 2002. Evidence on the demand for money function in Uganda. UNICEF Zimbabwe Working Paper No.2002-01.

Kot, A. 2004. The impact of monetization on the money demand in Poland. Makroekonomia, July: 31-37.

la Cour, L. F. 2006. The problem of measuring ‘money’: Results from an analysis of Divisia monetary aggregates for Denmark. In M. T. Belongia & J. M. Binner (Eds.), Money, measurement and computation: 185-210. New York: Palgrave MacMillan.

Marashdeh, O. 1997, November. The demand of money in an open economy: The case of Malaysia. Paper presented at the Southern Finance Association Annual Meeting, Baltimore, Maryland.

Schunk, D. L. (2001). The relative forecasting performance of the divisia and simple-sum monetary aggregates. Journal of Money, Credit and Banking, 33 (2): 272-283.

Sriram, S. S. (2002). Determinants and stability of demand for M2 in Malaysia. Journal of Asian Economics, 13: 337-356.

Thornton, D. L., & Yue, P. (1992). An extended series of divisia monetary aggregates. Federal Reserve Bank of St. Louis Review, November/December: 35-52.

Yu, Q., & Tsui, A. K. (2000). Monetary services and money demand in China. China Economic Review, 11: 134-148.

14