Long-run Stability of Demand for Money in China with Consideration of Bilateral Currency Substitution

Long-run Stability of Demand for Money in China with Consideration of Bilateral Currency Substitution Yongqing Wang The Department of Business and Economics The University of Wisconsin-Sheboygan Sheboygan, WI 53081 ABSTRACT We have recently examined money demand in China using quarterly data up to 2002IV and employing stability tests in conjunction with cointegration analysis, and found that M1, but probably not M2, money demand in China is stable. In the present study, we further examined money demand in China by incorporating into the money demand model foreign interest rates and exchange rates on a country-specific, bilateral basis. CUSUM and CUSUMSQ tests with cointegration analysis show that Chinese money aggregate M1 is cointegrated with its determinants and stable in the longrun. There is some doubt with the cointegration of M2 with its determinants. The domestic income and interest rate play a crucial role in China s money demand. However, Chinese money market appears to lack significant currency substitution with most of the 20 foreign economies examined. Keywords: Money demand; Currency substitution; Cointegration; Stability; CUSUM; CUSUMQ JEL Classification: E41 F39 1

I. Introduction Money demand is a very important Macro topic because it forms the link between the monetary aggregates and the other important macro variables such as income and interest rate. Money demand in many countries has been extensively studied. Following other countries, money demand in China also attracts some researchers attention. This attention grows quickly especially after 1980 s. The main reason is that after the economic reform in 1978, Chinese economy has grown rapidly and monetary variables have expanded dramatically. The literature on money demand in China includes but is not limited to Chow (1987), Chen (1989), Chan et al. (1991), Ma (1993), Huang (1994), Xu (1998) and Huang (2000). In general, these papers have employed standard estimation technique or recent cointegration techniques to estimate money demand in China from different aspects such as definition of monetary aggregate, the variables that should be included in the money demand function, the effects of economic reform on money demand, and the causal relationship between monetary aggregate and some other macroeconomic variables. These studies have helped us understand the money demand in China. However, there are several limitations. First, the data used in most of the previous studies only runs up to mid-1990s. Second, there is almost no paper using stability test, such as that developed by Brown et al (1975), to test the stability of money demand in China. Instead, in previous studies, money demand is stable as long as the monetary aggregate is cointegrated with the independent variables in the money demand model they used. However, cointegration does not imply stability, as demonstrated by Bahmani-Oskooee and Bohl (2000). Third, China has gradually opened up to the world. It has been suggested that money demand would be less stable if currency substitution exits. This argument makes it important to test the long-run stability of money demand when the international variables are included in the model. In a recent study by Bahmani-Oskooee and Wang (forthcoming), we employ CUSUM and CUSUMSQ stability tests in conjunction with cointegration analysis to analyze the money demand in China. Quarterly data over the period of 1983I-2002IV is used. In addition, foreign interest rates and exchange rates are incorporated into the model. We find that M1 money demand in China is 2

stable, while the stability of M2 money demand is somewhat questionable. The present study is focused on currency substitution by examining the role of interest rate of each of several specific countries and the bilateral exchange rate between China and that country. The money demand model that includes overall foreign interest rates and exchange rates, only gives us a general idea of currency substitution. However, each country is different and thus may have different relationship with China. An obvious currency substitution between China and a country may be offset by insignificant currency substitution between China and some other countries. If we consider the money demand at the bilateral level by considering a specific foreign country s interest rate and bilateral exchange rate, it will give us a better idea of Chinese money demand. In order to have a broad idea of Chinese money demand, 20 countries were considered individually. Thus, the main purpose of this paper is to consider the stability of money demand in China with the consideration of bilateral currency substitution. To this end, Section 2 formulates the demand for money and introduces a relatively new bound testing approach to cointegartion technique. Section 3 presents the empirical results and Section 4 concludes. Data definition and sources are cited in an appendix. 2. Model Specification and the Bound Testing Approach Following the previous literatures, money demand of a country mainly depends on domestic income and interest rate. If a country is open enough, foreign interest rate and/or exchange rate should also be included into the model. In order to focus on the bilateral currency substitution, the modified money demand model is outlined by equation (1): LnM t = a + blny t + cr t + dfr it + elnex it + ε t (1) where M is the Chinese monetary aggregate in real term (M1 or M2); Y is the real income in China; R is the domestic interest rate, FR i is the interest rate of foreign country i and EX i is the bilateral 3

nominal exchange rate between China and foreign country i. Regarding the sign of the coefficients, we expect b to be positive, c and d to be negative and e to be either positive or negative. The reasons are as following. Demand for money comes from transaction demand for money and speculative demand for money. As real income increases, the transaction demand for money usually rises. Thus, demand for money will increase as real income increases. Hence, an estimate of the coefficient of real income Y (b) is expected to be positive. As domestic interest rate increases, the demand for money will decrease since speculative demand for money falls. So, an estimate of coefficient of domestic interest rate R (c) is expected to be negative. If there is an increase in foreign interest rate, the domestic residents in an open country will react to this change. Specifically, if currency substitution exists, the domestic residents will decrease their holdings of domestic currency in order to increase their holding of foreign currency. In this case, an estimate of coefficient of foreign interest rate FR (d) should to be negative. A change in exchange rate (EX) usually has two effects. Note that in this paper, exchange rate is defined as number of Chinese currency Yuan per foreign currency. Under this definition, a decrease in EX implies a depreciation of domestic (Chinese) currency or an appreciation of foreign country currency. On one hand, depreciation of domestic currency increases the domestic currency value of foreign assets held by domestic people, increases the wealth, and thus the demand for domestic currency. If this effect dominates, e should be positive. On the other hand, when a currency depreciates, the public tend to increase holdings of foreign currency and decrease holdings of domestic money. By doing so, the domestic residents try to avoid further domestic currency depreciation and increase the wealth. If this effect dominates, e should be negative. Equation (1) states the long-run relationship among the variables. Although this study focuses on the money demand in china in the long-run, we should also consider the short-run dynamic of equation (1) in order to carry out the testing procedure. Following Pesaran et al. (2001), it takes following form: 4

ΔLnM t = α + + n k = 0 β n k = 1 4, k β 0, k ΔLnM ΔLnEX it k t k + 0 n k = 0 + δ LnM β ΔLnY 1, k t 1 1 t k + δ LnY + t 1 n k = 0 β 2, k + δ R 2 ΔR t 1 t k + 3 n k = 0 + δ FR β ΔFR it 1 3, k 4 it k + δ LnEX it 1 + μ t (2) Unlike the Error Correct Model by Engle-Granger, Pesaran et al. (2001) introduces a technique that does not require pre-unit root testing. In order to justify cointegration among variables in (2), Pesaran at al. (2001) applies F-test. The null hypothesis of no cointegration (δ 0 = δ 1 = δ 2 = δ 3 = δ 4 = 0) is tested against the alternative hypothesis (δ 0 δ 1 δ 2 δ 3 δ 4 0). If there is cointegration among the variables, the CUSUM and CUSUMSQ tests will be applied to the residuals of equation (2) in order to test the stability. The CUSUM test is based on the cumulative sum of recursive residuals while CUSUMSQ test is based on the squared recursive residuals. Following Brown et al. (1975), if and only if the plot of the CUSUM and CUSUMSQ statistics stays within 5% significant level, then the long-run and short-run coefficient estimates are stable. The 5% significance level was portrayed by two straight lines based on the equations given in Brown et al (1975). 3. Empirical Results and Stability Tests Quarterly data from 1983 to 2003 were employed to carry out the empirical analysis. In order to examine the long run stability of money demand under bilateral currency substitution, China s largest 20 trade partners are considered. Real M1 and real M2 are used as money aggregate. Two steps are involved in the empirical tests. First, we impose 8 lags on each first differenced variable in (2) and carry out the F-test. Table 1 reports the results of the F-test for 8 lag length. Table 1 goes here From Table 1, it is clear that there is only one case in which the calculated F test with 8 lags 5

is greater than the upper bound critical value of 4.01 supporting cointegration. In the remaining cases, cointegration among the variables of M1 and M2 money demand function is rejected. However, as pointed out by Bahmani-Oskooee and Brooks (1999) and Bahmani-Oskooee (2001), the above results may be considered preliminary since lags are selected arbitrarily. We decide to overcome the arbitrary nature of lag selection and employ a selection procedure to select the appropriate number of lags on each variable. The selection procedure employed in this study is Akaike Information Criterion (AIC). So, we employ AIC to select the optimum number of lags for each variable in equation (2) after imposing maximum of eight lags on each first differenced variable. The optimal lags and F-test with optimal lags are also reported in table 1. From the results of F-test with optimal lags in table 1, the values of F-test with optimal lags are all greater than upper bound value except one case (Singapore) when M1 aggregate is used. However, there are only four cases supporting cointegration when M2 aggregate is used. These four cases are Belgium, Germany, Japan and UK. Since the main objective of this paper is to test the long-run stability, we only report the long-run estimation here. Table 2 reports the long-run coefficient estimates for M1 aggregate, while Table 3 reports the long-run estimates for M2 aggregate. Table 2 and 3 go about here From Table 2, the coefficient of LogYc all carries expected positive sign. More importantly, in 17 out of 20 cases, it is significant (denoted by * in the table). The coefficient of R carries expected negative sign in 19 cases, in which 14 cases are significant. The sign of FR is mixed and there are only 2 cases that the coefficient of FR carries negative and expected significant sign. The sign of EX is mixed as expected. There are 5 cases that the EX coefficient is significant. These countries are Canada, Denmark, Korea, Turkey and USA. It is interesting to notice that all these significant coefficients carries positive sign, suggesting that the depreciation of Chinese currency Yuan (or appreciation of foreign country currency) do increase some Chinese people s wealth by holding these five countries assets and thus increase the money demand for Chinese currency. 6

When we combine the results for M1 aggregate, it suggests that the real income and domestic interest rate play an important role in the demand for money in China. There is not much evidence suggesting currency substitution. From Table 3, the coefficient of LogYc all carries expected positive sign, among which 18 cases are significant. The sign of R is negative, as expected, in 19 cases. However, there are only 8 cases that are significant. The sign of FR is mixed and none of them is significant. The sign of LogEX is also mixed as we expected. However, there are only three cases in which the coefficient of LogEX is significant. When we combine the results for M2 aggregate, it also suggests that the real income and domestic interest rate play an important role in the demand for money in China. There is not much evidence suggesting currency substitution when M2 aggregate was used. The main results from Table 2 and Table 3 are consistent. Chinese monetary aggregates mainly are affected by domestic real income and interest rate. Chinese money market appears to lack significant currency substitution. After the long run estimates, we examine the stability by applying CUSUM and CUSUMSQ tests proposed by Brown, et al (1975). Despite the lack of cointegration of M2, we also carried out the stability tests for M2. The results of stability are summarized in Table 4. For both M1 and M2 aggregates, it is stable in most cases. Specifically, for M1 aggregate, it is stable except three cases: Belgium, Japan, and Philippines. For M2 aggregate, it is stable except four cases: Canada, Indonesia, Philippines and Singapore. The stability results and cointegrations results suggest that M1 may be better money aggregates for China since M1 is cointergated and stable in most cases while M2 is only cointegrated in some cases. These results are interesting. The results of M1 are consistent with the results in Bahmani-Oskooee and Wang (forthcoming). However, M2 results are somewhat different. The cointegration of M2 is questionable in this study while the stability of M2 is questionable in Bahmani-Oskooee and Wang (forthcoming). 4. Conclusions 7

China s demand for money has received increasing attention since 1980s. We have recently examined money demand in China using quarterly data up to 2002IV and employing stability tests in conjunction with cointegration analysis. In the present study, we further examined money demand in China by incorporating into the money demand model foreign interest rates and exchange rates on a country-specific, bilateral basis. We show that the M1 monetary aggregate of China is cointegrated with domestic income and interest rate as well as interest rate and bilateral exchange rate for most foreign countries. M2 money aggregate is only cointegrated in some cases. Domestic income and interest rate play a crucial role in China s money demand. However, there is not much evidence for currency substitution. The CUSUM and CUSUMQ test with cointegration analysis revealed that both M1 and M2 aggregates are stable in most cases. Based on the cointegration results and stability tests results, M1 is served as better money aggregate for China 8

Appendix Data Definition and Sources All quarterly data are from 1983 to 2003 and from the following sources: a. International Financial Statistics (IFS CD-ROM) b. Yi (1993) c. Chinese Statistics Yearbook M1: Narrow money in real term. Nominal M1 comes from source a. After deflated Nominal M1 by consumer price index (CPI), M1 is obtained. The price index for1983q1-1989q4 period are from Source b and for the remaining period come from source a. M2: Broad money in real term. Nominal M2 is defined as the sum of Nominal M1 and nominal quasi money (both from Source a). M2 is nominal M2 deflated by CPI. Y: Quarterly Real GDP. We generated quarterly data from annual data using the method by Bahmani-Oskooee (1998, p. 142) due to the absence of quarterly data. Annual GDP data come from Source C. R: Domestic interest rate (defined as deposit rate), which comes from Source a. FR i : Foreign interest rate of country I (defined as deposit rate). The data comes from the Source a. EX: Bilateral nominal exchange rate defined as # Yuan per foreign currency. It comes from Source a. Note that under this definition, a decrease in EX indicates a depreciation of domestic currency. 9

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Table 1: The result of F-test Country Australia Belgium Canada Denmark France Germany India Indonesia Italy Japan Korea Malaysia Netherlands Norway Philippines Singapore Thailand Turkey UK USA F-test with 8-lags 2.9328 2.5397 3.1771 2.3704 1.3985 1.4285 2.9401 0.3687 1.9077 1.7438 0.9518 1.4758 1.4299 0.2916 1.0396 0.3108 0.2992 0.8463 4.0891 2.5380 M1 Money Aggregate Optimal lags (3,4,0,3,7) (4,2,5,8,2) (8,4,1,0,3) (7,6,2,6,0) (7,4,0,0,0) (7,4,6,1,6) (8,4,2,5,5) (7,4,0,0,2) (8,4,6,8,7) (8,7,1,8,2) (8,4,1,0,7) (5,8,1,0,6) (5,4,0,1,6) (8,6,2,6,0) (8,4,4,8,6) (8,4,0,1,6) (8,6,1,0,8) (7,4,1,0,0) (8,4,5,7,4) (8,4,1,8,0) F-test with optimal lags 6.0672 9.0822 6.1983 7.8767 4.8686 4.9566 9.1142 5.5629 4.8130 44.2264 5.4265 4.9217 4.6905 4.4857 4.5361 3.3572 4.0228 5.9174 6.7040 8.2391 F-test with 8-lags 1.3667 2.5600 0.5172 0.9915 1.5507 3.1150 3.5405 0.4027 1.1995 1.6025 0.3498 1.6224 2.0947 0.0604 1.2583 0.4981 0.5845 0.2598 2.6014 1.4768 M2 Money Aggregate Optimal lags (8,5,0,0,0) (7,8,6,8,4) (8,5,0,6,0) (8,5,0,0,0) (8,5,0,0,1) (6,8,0,8,6) (8,5,0,2,5) (8,5,0,5,0) (5,6,0,8,5) (5,8,0,7,8) (8,5,0,0,0) (8,5,0,5,3) (5,4,1,2,1) (8,6,0,3,0) (8,4,4,8,1) (8,5,0,0,0) (8,5,0,6,0) (7,5,0,3,0) (8,5,0,5,3) (8,5,0,6,0) F-test with optimal lags 0.4820 5.7723 1.0250 1.1781 0.5513 4.6837 3.2936 1.6923 2.1002 4.2817 0.7354 1.8066 0.2748 0.2138 3.6349 0.6926 1.4749 1.4887 4.5320 1.7836 12

Table 2: Long-run Coefficient Estimates for M1 Country LogYc R FR LogEX Inpt Australia Belgium Canada Denmark France Germany India Indonesia Italy Japan Korea Malaysia Netherlands Norway Philippines Singapore Thailand Turkey UK USA 0.87 (2.21)* 2.05 (5.77)* 1.12 (7.49)* 1.58 (22.16)* 1.19 (7.67)* 1.34 (3.51)* 1.71 (16.01)* 1.35 (15.43)* 1.97 (0.54) 1.65 (18.58)* 0.91 (3.63)* 0.98 (1.64) 1.30 (4.96)* 1.40 (16.48)* 1.63 (9.21)* 0.64 (0.51) 1.08 (2.68)* 1.24 (13.11)* 1.58 (17.63)* 1.47 (12.64)* -0.14 (2.25)* -0.13 (2.10)* -0.08 (4.83)* -0.05 (8.37)* -0.05 (2.25)* -0.15 (0.97) -0.12 (4.52)* -0.07 (2.08)* 0.72 (0.20) -0.07 (6.43)* -0.13 (2.79)* -0.22 (0.92) -0.05 (2.73)* -0.05 (5.17)* -0.05 (1.47) -0.16 (0.80) -0.13 (1.16) -0.06 (4.32)* -0.04 (9.77)* -0.04 (4.80)* 0.001 (0.06) 0.31 (1.71) -0.03 (1.36) 0.05 (4.81) -0.14 (1.68)* 0.06 (1.01) 0.15 (3.34) 0.00 (0.51) -0.49 (0.22) 0.14 (4.03) 0.00 (0.21) 0.11 (0.76) -0.21 (1.95)* 0.01 (1.43) 0.02 (2.06) -0.02 (0.28) 0.01 (0.32) 0.00 (0.29) 0.03 (3.03) 0.05 (1.43) 1.22 (1.54) -0.40 (1.47) 0.43 (2.86)* 0.17 (3.27)* -0.17 (0.87) -0.17 (0.54) -0.07 (0.33) 0.04 (0.18) -3.82 (0.21) 0.01 (0.17) 0.76 (2.07)* 1.92 (0.74) 0.08 (0.23) 0.16 (1.46) -0.21 (0.61) 1.07 (0.58) 0.87 (0.84) 0.33 (2.72)* 0.00 (0.01) 0.29 (1.68)* -0.05 (0.07) -1.49 (2.03) 0.31 (0.52) -0.87 (3.26) 1.37 (1.60) 1.06 (0.47) -2.61 (3.24) 0.41 (0.23) 4.52 (0.22) -0.72 (1.66) 5.58 (2.01) 0.54 (0.46) 0.72 (1.31) -0.22 (0.62) -1.34 (1.08) 1.35 (0.59) 2.71 (0.79) 4.20 (2.58) -0.84 (4.91) -1.24 (1.97) 13

Table 3: Long-run Coefficient Estimates for M2 Country LogYc R FR LogEX Inpt Australia Belgium Canada Denmark France Germany India Indonesia Italy Japan Korea Malaysia Netherlands Norway Philippines Singapore Thailand Turkey UK USA 1.73 (5.52)* 2.20 (37.97)* 1.88 (6.65)* 1.86 (9.00)* 1.79 (8.09)* 1.89 (38.29)* 2.10 (10.71)* 1.77 (11.97)* 1.82 (24.10)* 2.13 (24.15)* 1.70 (10.26)* -0.39 (0.03) 1.65 (1.84)* 1.44 (1.13) 2.38 (10.76)* 1.46 (4.14)* 1.65 (7.73)* 1.62 (14.74)* 2.18 (15.53)* 1.86 (10.83)* -0.04 (1.45) -0.03 (4.79)* -0.02 (1.72)* -0.03 (1.95)* -0.02 (0.76) -0.03 (4.79)* -0.06 (1.84)* -0.04 (1.27) -0.01 (1.17) -0.02 (2.78)* -0.04 (1.68)* -0.47 (0.18) -0.05 (0.31) -0.08 (0.48) 0.04 (1.13) -0.06 (1.48) -0.07 (1.56) -0.03 (2.93)* -0.01 (1.07) -0.00 (0.20) 0.00 (0.09) 0.15 (5.73) 0.02 (0.42) 0.02 (0.97) -0.03 (0.29) 0.06 (6.54) 0.15 (1.78) 0.02 (1.48) -0.02 (1.40) 0.11 (5.02) 0.01 (0.54) 0.32 (0.18) -0.10 (0.29) -0.002(0.03) 0.05 (2.55) -0.003(0.07) 0.02 (0.81) 0.00 (0.93) 0.04 (3.15) 0.04 (1.09) 0.25 (0.77) -0.09 (1.43) 0.25 (0.81) 0.27 (1.87)* -0.15 (0.44) 0.01 (0.36) -0.64 (1.02) 0.30 (1.01) 0.08 (1.17) 0.01 (0.12) 0.30 (1.09) 4.57 (0.17) -0.20 (0.14) 0.76 (0.47) -0.80(2.02)* 0.59 (1.22) 0.63 (1.39) 0.46 (3.72)* -0.17 (1.05) 0.38 (1.15) -0.41 (0.38) -1.96 (12.21) -1.01 (1.05) -0.64 (0.79) 0.30 (0.23) -0.96 (7.80) -3.82 (2.22) 1.68 (0.67) -0.78 (2.98) -1.50 (3.15) 1.52 (0.84) 6.77 (0.16) 1.61 (0.25) -0.33 (0.10) -3.96 (2.79) 0.10 (0.12) 1.35 (0.83) 5.31 (3.13) -1.44 (4.37) -1.58 (2.13) 14

Table 4: Stability Test Results Country Australia Belgium Canada Denmark France Germany India Indonesia Italy Japan Korea Malaysia Netherlands Norway Philippines Singapore Thailand Turkey UK USA M1 Money Aggregate M2 Money Aggregate CUSUM CUSUMQ CUSUM CUSUMQ Unstable Unstable Unstable Unstable Unstable Unstable Unstable 15