UNIVERSITY OF MACAU FACULTY OF BUSINESS ADMINISTRATION The Relationship between Reserve Requirement Ratio and Inflation in China Wang Yong Jing M-A9-5721-1 Thesis presented to the Faculty of Business Administration University of Macau In partial fulfillment for granting the Degree of Master of Science in Finance 2011 ACKNOWLEDGEMENT I own a great deal to a number of people for the various kinds of help they have given to me this year. First of all, my heartfelt thanks and appreciation go to my supervisor, Prof. Liu Ming Hua, for his patient tutorship, in-depth comment and invaluable advice in preparing my manuscript through the beginning to the end. It has been a great privilege and job to study under his guidance and encouragement. Without his expert comments, suggestions and modifications, my thesis would not have emerged in its present form. Meanwhile, special thanks also go to the Department of FBA and all the teachers who have given me lectures during the Master course; I would like to express my gratitude of their enlightening instruction and warm-hearted assistance. Last but not least, many thanks go to my families for their sympathetic understanding and unfailingly support during my studies and during my preparation of my thesis. I UNIVERSITY OF MACAU ABSTRACT This thesis studies how the central bank sets the reserve requirement ratio in China. The monthly series of reserve requirement ratio and inflation data from January 1999 to May 2011 are used to examine both the long-run relationship and the short-run dynamics between changes in reserve requirement ratio and inflation pressure. Our results show that there is a quite weak long-term relationship between reserve requirement ratio and inflation, reflecting the fact that as a tool of monetary policy, reserve requirement ratio is rather ineffective in China. This may be attributed to the macro-economic regulatory objective of keeping the currency at a relative stable level, stimulating economic growth and creating more job opportunities. In the short-run, the reserve requirement ratios of both large and complex financial institutions and small and medium financial institutions respond somewhat to inflation pressures, and changes in inflation has a greater influence on changes in reserve requirement ratio for small and medium financial institutions than that for large and complex financial institutions. However, the extent of disequilibrium at time (t-1) has little effect on reserve requirement ratio. II LIST OF FIGURES AND TABLES Figure 1: Reserve requirement ratios and inflation rates…………………………….14 Table 1: General descriptive statistics of sample data………………………………..15 Table 2: Correlation coefficients of sample data……………………………………..15 Figure 2: Flow chart of the whole process…………………………………………...17 Table 3: Unit root test………………………………………………………………...18 Table 4: Johansen co-integration test………………………………………………...19 Table 5: Pair-wise Granger causality test…………………………………………….20 Table 6: Long-term relationship…………..………………………………………….21 Table 7: Symmetric adjustment speed……………………………………….……….22 Table 8: Asymmetric adjustment speed………………………………………………24 Table 9: Mean adjustment lags in months………………………………………...….27 III LIST OF ABBREVIATIONS The following is a list of abbreviations used throughout this thesis. The abbreviations are also explained on their first subsequence chapters. PBOC People’s Bank of China RMB Renminbi CPI Consumer Price Index GDP Gross Demestic Product LCFI Large and Complex Financial Institutions SMFI Small and Medium Financial Institutions RRR Reserve Requriement Ratio RRR1 Reserve Requriement Ratio of Large and Complex Financial Institutions RRR2 Reserve Requriement Ratio of Small and Medium Financial Institutions OLS Ordinary Least Squares ECM Error-correction Methodology VARs Vector Auto Regressions ADF Augmented Dickey Fuller (one type of Unit Root test) P-P Phillips –Perron (one type of Unit Root test) AIC Akaike Information Criterion SIC Schwarz Information Criterion HQC Hannan-Quinn Criterion IV CONTENTS CHAPTER 1: INTRODUCTION ............................................ 1 CHAPTER 2: LITERATURE REVIEW .................................. .6 2.1 Interest Rates Channel............................................. ………………………………………6 2.2 Competition Enhances Monetary Policy Transmission …………………………………...6 2.3 Asymmetric Manner and Causes .......................................................... …………………..7 2.4 Studies on Chinese Interest Rates Setting ...................................................... …………….7 CHAPTER 3: RESERCH METHODOLOGY........................... .9 3.1 Long-run Model ................................................................................................................ ..9 3.2 Short-run Model under Symmetric Assumption ............................................................... ..9 3.3 Short-run Model under Asymmetric Assumption ............................................................. 11 CHAPTER 4: FINDINGS.................................................... 14 4.1 General Description of Sample Data................................................................................. 14 4.2 Correlation Coefficients .................................................................................................... 15 4.3 Unit Root Test ................................................................................................................... 16 4.4 Johansen Co-integration Test ............................................................................................ 18 4.5 Granger Causality Test…… .............................................................................................. 19 4.6 Estimated Coefficients of Long-term Relationship. .......................................................... 21 4.7 Symmetric Error Correction Model .................................................................................. 22 4.8 Asymmetric Error Correction Model ................................................................................ 24 4.9 Mean Adjustment Lags ..................................................................................................... 26 CHAPTER 5: CONCLUTIONS ............................................ 28 REFERENCES ................................................................ 29 V CHAPTER 1: INTRODUCTION When facing with economic and social goals in mature market economies, such as promoting sustainable economic growth, managing aggregate demand, controlling inflation in a desired range and keeping low unemployment rate, monetary authority uses monetary policy and the interest rate, in particular, as the main instrument. Other frequent tools of monetary policy include open market operations, rediscounting and reserve requirements. For example, as a pioneer of inflation targeting countries, New Zealand achieves this objective through periodic adjustments to the interbank rate set by the central bank in order to control inflation within a certain band. Nowadays, inflation targeting is extensively implemented in Canada, Australia, Turkey, and so on. The United States of America adopts mixed policy by changing usual interest rate, aiming at controlling both inflation and unemployment. However, both the goals and instruments of monetary policy in developing countries may be different from those in developed countries due to the immature economic environment. Perhaps that is why the effect in implementing monetary policy is not rather ideal during the process of managing the macro-economy. Chinese government assigns the task of the formation and implementation of monetary policy as an obligation to the country’s central bank- the People’s Bank of China (PBOC), which unlike central banks in developed countries, PBOC is affiliated with Chinese government. China now is monetary targeting and targets a currency basket, which means the primary concern for China is to keep the domestic currency, Renminbi (RMB) at a stable level in the achievement of promoting the sustainable economic growth. China has maintained a relative high economic growth rate ever since 1978, reforming from a centrally planned economy to a more market oriented economy. In order to fulfill the 12th 5-year economic plan, the Gross Domestic Product (GDP) has to keep at 7% increase a year from now on. However, there is no specific declaration made with regard to controlling inflation. 1 Monetary policy can come into effect by altering the proportion of total assets that banks must hold in reserve with the central bank-the reserve requirement ratio (RRR), which will further influence the country’s interest rates and borrowings through changing the amount of available loans. Once central banks increase the reserve requirement ratio, both the available loans provide by commercial banks and the ability of creating credit decrease. The larger the RRR is, the smaller the money multiplier is. Thus the ability of creating credit and expanding credit size in the whole commercial bank system is weakened.1 As a result, money tightened, money supply decreases, interest rates increase, and investments and social spending reduce accordingly, or vice versa Western central banks rarely change the reserve requirements because this would not only cause immediate liquidity problems for banks with low excess reserves, but also generate exponential effect due to the lending multiplier. They generally prefer to use open market operations to implement their monetary policy. But the People’s Bank of China uses reserve requirements as a main inflation-fighting tool. Inflation refers to a general rise in level of prices measured against a standard level of purchasing power. Inflation is measured by computing the percentage change in cost under the circumstance of no increase in quality. The most well known measurements of inflation are the CPI which measures consumer prices, and the GDP deflator, which measures inflation in the whole of the domestic economy. In this study, we use monthly CPI as a measurement of inflation rate. 1 MS=MB*mm, mm=(1+c)/(c+RRR); where MS represents money supply, MB denotes monetary base, mm means the money multiplier, c is the rate at which people hold cash in hand, and RRR is the shorthand for reserve requirement ratio. We can see from the formula that MS would have a large change although there is only a small change in RRR. 2 Inflationary pressures are still large in China due to China’s strong economic growth momentum. The average inflation rate in China was 1.83% from January 1999 to May 2011, with a historical high of 8.70% in February of 2008 and a record low of -2.2000% in March of 1999. At the same time, Chinese central bank has moved to cool inflation by raising the proportion of funds that the country’s lenders must keep in reserve rather than lending out. From January 1999 to May 2011, the average RRR of large and complex financial institutions (LCFI) in China was 9.97% and 9.93% of small and medium financial institutions (SMFI), reaching a historical high of 21.00% and 17.50% for LCFI and SMFI respectively, and a record low of 6.00% for both. The RRRs of LCFI and SMFI had been increased gradually ever since 2009, which will certainly intensify competition among deposit money market. Although increase in RRR is direct for financial institutions, the effect on clients is indirect. The sustainable increase in RRR will lead to an increase in interest rates later, which would pose a direct effect on clients. This contributes to reduce inflation expectation by bringing expectations of further restrictions in the market, which is definitely a piece of good news from the perspective of long run. As the Chinese central bank adjust the reserve requirement ratio to control inflation more often than short-term interest rate due to the immaturity of money markets in China, it would be interesting to see if the central bank of China adjusts the reserve requirement ratio similarly as other countries adjust short-term interest rate when struggling against inflation. This study examines how the central bank sets the reserve requirement ratio in China. It contributes to the literature on the transmission mechanism of monetary policy by applying a two step approach for long-term and short-term respectively. In the long run, we employ an Ordinary Least Squares (OLS) regression to estimate the relationship between reserve requirement ratio (RRR, both the RRR of large and 3 complex financial institutions and that of small and medium financial institutions) and inflation rate. In the short run, a standard error-correction methodology (ECM) is adopted to assess the dynamics of RRR changes in response to inflationary pressures. We also examine whether the administered RRR adjustment speed differ when they are above or below their long-term equilibrium levels by adding a dummy variable to the standard error-correction model. Our results show that there is a quite weak long-term relationship between reserve requirement ratio and inflation. In the short run, there is always an upward rigidity in the reserve requirement ratios of large and complex financial institutions. For small and medium financial institutions, the reserve requirement ratio follows a mean-reverting process. The central bank of China tends to adjust reserve requirement ratios down when they are above their equilibrium levels and adjust them up when they are below their equilibrium levels. The weak long-term relationship reflects the fact that as an instrument of monetary policy, reserve requirement ratio is rather ineffective in China, which may be attributed to the macro-economic regulatory objective of “keeping financial currency at a relative stable level, stimulating economic growth and creating more job opportunities”.2 The reserve requirement ratios of SMFI are adjusted upwards a bit slower than downwards, suggesting that the PBOC is quick to bring RRR2 down but slow to bring it up. The asymmetry in adjustment speed of reserve requirement ratios of SMFI to inflation is mainly due to the fact that a relatively low reserve requirement environment is easy for SMFI to play an active role in economic activities and necessary for economic growth, thereby preserving the stability of the economic environment and creating more employment opportunities for the huge and growing population. The rest of the thesis is organized as follows: Chapter 2 attempts to detail the 2 Refer to: http://www.voanews.com/english/news/asia/Inflation-Rises-in-China-as-Economic-Gr owth-Shows-Signs-of-Slowing-96131384.html 4 institutional background of monetary transmission mechanism of different countries, especially in China. Chapter 3 outlines the overall research design: explain the overall research methodology; the data collection process and the analytical methods. Chapter 4 identifies the empirical implementation and the findings are presented in this section, too. Chapter 5 summarizes the results and draws some conclusions. 5 CHAPTER 2: LITERATURE REVIEW There are numerous studies on how the central bank set interest rates (both deposit rates and lending rates) in the literature. 2.1 Interest Rates Channel Hannan and Berger (1991) studied the setting of deposit interest rates by banks to look into how price rigidity differs across firms and markets and between upward and downward changes of price. They claimed that deposit rates were more rigid upwards than downwards, they also addressed the unresolved issue of asymmetry between upward and downward price changes. Similarly, Scholnick (1996) applied an asymmetric error correction technique to test whether mean adjustment lags were different when retail rates were above or below their equilibrium levels, and found that deposit rates were more rigid when they were below their equilibrium level than when they were above. For lending rate adjustment the findings are mixed. Heffernan (1997) applied an error correction model to capture the dynamics of deposit and loan rates responses to changes in the central bank’s base rate, and discovered that the adjustment for both the deposit and lending rates was symmetric and the speed was roughly the same. However, he found a wide variation in the adjustment speed within each type of financial products. 2.2 Competition and Monetary Policy Transmission It is possible in situations where deposit and lending rates are set directly by the central bank, there is little competition in the banking system, which would hamper the transmission of monetary policy. Many studies show that competition in the banking industry could enhance the transmission of monetary policy (Berger and 6 Hannan, 1991; Sharpe and Neuman, 1992; Cottarelli and Kourelis, 1994; Bondt, 2002). 2.3 Asymmetric Adjustment and Causes The central bank may adjust interest rates in an asymmetric manner for the purpose of creating employment opportunities and promoting economic growth. For instance, deposit rates may be adjusted downwards more quickly than they are adjusted upwards. Berger and Hannan (1991) and Chong et al. (2006) found that deposit rate adjustment could be asymmetric and delayed due to imperfect competition and switching costs in the developed countries. 2.4 Studies on Chinese Interest Rates Setting A few other studies have examined how the People’s Bank of China sets the interest rates. Handa and Wang (2007) studied monetary policy reaction function of China and found that the interest rates set by PBOC follow a Taylor-type rule, but the coefficients were different from those in the US.3 Under assumption that the Taylor rule was symmetric, they did not examine whether the adjustment speed was symmetric or not. Liu et al (2009) examined how the Chinese central bank adjusts the 1-year time deposit rate (regarded as the benchmark interest rate in China due to its immaturity of money markets) in response to changes in inflation. They reported that there was a long-term relationship between interest rates and inflation, but the relationship was Handa and Wang (2007) found that comparing to US monetary policy, interest rates set by PBOC were far less sensitive to changes in inflation which might be explained by government intervention. However, Yanzgan and Yilmazkuday (2007) found a much higher policy response rate for the developing countries of Turkey and Israel whose economies were both less-regulated. 3 7 quite weak. The PBOC adjusted the interest rate downwards faster than they adjusted them upwards in the short-term. The relationship between reserve requirement ratio and inflation of China is relatively under-explored. As the Chinese central bank adjust the reserve requirement ratio to control inflation more often than interest rates due to the immaturity of money markets in China, it would be interesting to see if the central bank adjusts the reserve requirement ratio similarly. 8 CHAPTER 3: RESEARCH METHODOLOGY This paper will examine both the long-run relationship between inflation and reserve requirement ratio and the short-term dynamics of reserve requirement ratio changes in response to inflationary pressures. Based on previous studies, Scholnick (1996), Heffernan (1997), Chong, Liu and Shrestha (2006), the following methodology is applied: 3.1 Long-run Model The long-term relationship between inflation and reserve requirement ratio can be estimated as follows: y t 0 1 xt t (1) where dependent variable yt stands for the reserve requirement ratio set by PBOC; xt represents the inflation rate; t denotes the disturbance error; 0 and 1 are the model parameters. 0 represents the constant markup and 1 is used to measure the degree of pass-through in the long term (see Rousseas (1985)). When 1 is equal to one, the long-run adjustment is complete. However, if 1 is less than one, the adjustment is incomplete due to high switching and menu costs, the not completely competitive markets or asymmetric information. 3.2 Short-run Model under Symmetric Assumption In case of inflation changes, Equation (1) is invalid. The central bank will not adjust reserve requirement ratios immediately according to frequent inflation changes. 9 Due to the non-stationary nature of most time series data, the following Johansen co-integration technique and error-correction procedures are used to remove any spurious results. The Johansen co-integration technique is used to test whether co-integrating relationship exists between the different RRRs and inflation rates.4 Once the co-integration has been determined, we employ a standard error-correction methodology to examine the short-run dynamics of administered government reserve requirement ratio changes in response to inflationary pressures. It can be simply written as follows: y 1 x ( y2 t t t 1 0 1 xt 1 ) vt (2) where denotes first difference operator; ˆt 1 ( yt 1 0 1 xt 1 ) is the residual of the long-run relationship given by Equation (1) and represents the extent of disequilibrium at time (t-1); vt is the error term. 1 represents the immediate response of changes in reserve requirement ratio to changes in inflation, it measures how much of the change in the underlying inflation rate gets reflected in the RRR in the same period. 2 reflects the error-correction adjustment speed when the RRRs are away form their equilibrium levels. The Johansen procedure is designed to statistically determine the number of co-integrating vectors r in the VAR. Johansen co-integration Test assumed that the vectors are all n I(1) time series, so yt can be written as a VAR, 4 yt 1 yt 1 2 yt 2 ... k yt k t , which can then be re-parameterized as q k yt yt k i yt i ut , where q k 1 , Bi 1 , B j denotes an i 1 j 1 (n*n) matrix from the lags of the VAR, B j for i 1...q , the rank r of the k j i 1 matrix determines the number of co-integrating vectors in the VAR. If r 0 then there are no co-integrating vectors. To determine the value of r , Johansen provides two different likelihood ratio tests. They are the trace test, with a test statistic LR T n ln(1 ) i r 1 i and the maximum eigenvalue test LR T ln(1 r 1 ) , where are eigenvalues from . Tests are conducted both under the null that r 0 and then r 1 . 10 Then the pass-through mean adjustment lag (MAL) can be determined as follows (Hendry and Doornik, 1994): MAL (1 1 ) / 2 (3) It is possible to derive an estimate of the mean adjustment lag from the coefficient of the symmetric residual series. It is well known that the co-integrating residuals are followed by definition I(0), which implies that the whole series is mean reverting. Thus if residuals are above their mean they will tend to move back towards the equilibrium relationship, and vice versa. The drawback with this specification is that it assumes that adjustment is symmetric when the RRRs are above or below their equilibrium values. 3.3 Short-run Model under Asymmetric Assumption However, the short-term adjustment speed may be asymmetric, in other words, the adjustment speed may differ when rates are above the equilibrium level and when rates are below that. We also test for differences in the RRRs adjustment speed no matter when they are above or below their equilibrium level by adding an indicator variable, to Equation (2). If the residual error term ( ˆt 1 yt 1 0 1 xt 1 ) is positive, we set the dummy variable equal to one and 0 otherwise. Therefore the asymmetric short-term dynamic equation can then be written as: yt 1xt 2 ˆt 1 3 (1 )ˆt 1 t (4) where 1 represents the immediate response of changes in RRR to changes in inflation, it measures how much of the change in the underlying inflation rate gets 11 reflected in the RRR in the same period, 2 reflects the speed of error correction adjustment when the RRRs are above their equilibrium levels and 3 captures the error correction adjustment speed when the RRRs are below their equilibrium levels. Once the equation (4) is estimated, it is possible to use a standard Wald test with a 2 (1) distribution to determine if 2 is significantly different from 3 . The error-correction model also allows us to determine how long it takes for the RRRs to completely adjust back to their equilibrium levels in case of changes in inflation. We can also estimate two mean adjustment lags, one when the serious are above their equilibrium levels, and the other when they are below their equilibrium levels. The asymmetric mean adjustment lags of a complete pass-through can thus be defined as follows: If the RRRs are above their equilibrium levels, we use MAL as the mean adjustment lag: MAL (1 1 ) / 2 (5) If the RRRs are below their equilibrium levels, we use MAL as the mean adjustment lag: MAL (1 1 ) / 3 (6) To assess the stationary properties of all the series, we use unit root processes, the Phillips Perron (P-P) and the Augmented Dickey-Fuller (ADF) tests. If the pair-wise variables are I(1), a Johansen co-integration test can then be conducted. To examine whether there is the co-integrating relationship of various RRRs or not, we use the Johansen co-integration tests. 12 To determine if changes in inflation cause adjustments in the RRRs or vice versa, we apply Granger causality tests, whose results are sensitive to the choice of the lag length. Based on standard information criteria such as final prediction error, Akaike Information Criterion (AIC), Schwarz Information Criterion (SIC), and Hannan-Quinn Criterion (HQC), we use the system-defined lag at first and then increase the lag length to eliminate persistence (i.e. serial correlation) in the error term of the bivariate Vector Auto Regressions (VARs). Ordinary Least Squares (OLS) regression results are used to estimate both the long-term and short-term relationship, i.e., Equations (1), (2) and (4). 13 CHAPTER 4: FINDINGS 4.1 General Description of Sample Data The monthly series data of reserve requirement ratio and inflation are both downloaded from the PBOC. The sample size is 149 for both the RRRs and inflation and the sampling period for both the RRRs and inflation is from January 1999 to May 2011, covering the period span of over 12 years. We investigate both the reserve requirement ratio of large and complex financial institutions (LCFI) and that of small and medium financial institutions (SMFI) in order to get a full script of the relationship between the RRR and inflation. All the RRRs are set by the central bank and the inflation rates are all measured by CPI. Figure 1 shows the monthly reserve requirement ratios and inflation rates chart over the sampling period from January 1999 to May 2011. Figure 1. Reserve requirement ratios and inflation rates 14 From Figure 1 we can see that before June 2006, the RRRs were kept at a relative stable level with only three changes in more than 7 years. The RRRs increased frequently from June 2006 to November 2008. After that the RRRs of LCFI were about 15.50% until December 2009 and the RRRs of SMFI were about 13.50% from December 2008 to October 2010, the RRRs had moved gradually higher since then. Regarding inflation rate, it changed with a relatively volatile speed around zero but without no clearly tendency. Table 1. General descriptive statistics of sample data Variable Mean Median Minimum Maximum Std. Dev. Observations RRR1 9.9732 7.5000 6.0000 21.0000 4.5384 149 RRR2 9.3960 7.5000 6.0000 17.5000 3.6817 149 Inflation 1.8309 1.4000 -2.2000 8.7000 2.5305 149 Notes: RRR1: reserve requirement ratio of large and complex financial institutions; RRR2: reserve requirement ratio of small and medium financial institutions. Table 1 shows the detailed descriptive statistics for the sample data. The average reserve requirement ratio of LCFI is 9.97% and the average reserve requirement ratio of SMFI is 9.40%, whereas the average inflation rate is 1.83% during the period from January 1999 to May 2011. The average RRR of both LCFI and SMFI are almost 8.00% above inflation. Inflation rate ranged from of 8.70% in February of 2008 to -2.20% in March of 1999. For both LCFI and SMFI, the RRRs have been adjusted upwards and reached the highest record of 21.00% and 17.50% in May 2011 respectively. Both the RRRs of LCFI and SMFI have relatively small and similar fluctuations. 4.2 Correlation Coefficients Table 2. Correlation coefficients of sample data 15 RRR1 RRR2 RRR1 1.0000 RRR2 0.9747 1.0000 Inflation 0.3909 0.4763 Inflation 1.0000 Notes: RRR1: reserve requirement ratio of large and complex financial institutions; RRR2: reserve requirement ratio of small and medium financial institutions. All the pair-wise correlation coefficients between the reserve requirement ratios and inflation rates are reported in Table 2. The inflation rates and reserve requirement ratios of both large and complex financial institutions and small and medium financial institutions are less correlated with low correlation coefficients, only 0.4976 for RRR1 and 0.5664 for RRR2 respectively. However, the correlation coefficient between RRR1 and RRR2 is 0.9820 which means that RRR1 and RRR2 are highly linearly correlated with each other. This further indicates that the central bank of China tends to adjust reserve requirement ratio of both large and complex financial institutions and small and medium financial institutions simultaneously. 4.3 Unit Root Test Now we have a general understanding of the time series data after 4.1-gernaeral description of sample data and 4.2-correlation coefficients. Then the next procedure can be easily and well explained by the following flow chart of the whole process: 16 Figure 2. Flow chart of the whole process Before analyzing the methodology used, we should ascertain whether the variable series are stationary first. To assess whether the RRRs and inflation rate series are stationary or not, the unit root test-the Augmented Dickey-Fuller (ADF) and the Phillips Perron (P-P) are used. If the variable series are stationary, we use time series model ARMA/ARIMA to analyze them; if not, we execute the Johansen co-integration tests on all the series then. If all the series represent an co-integrating relationship between each other, indicating that there is a long-term equilibrium relationship between the series, so we can apply Granger causality test to see which variable is conductive to forecast and explain the other variable, or vice versa; otherwise, the long-run relationship between them maybe not very obvious and strong, we’d better use other model and methodology instead to analyze them. 17 Given the descriptive statistics of the variable series, unit root test is used to assess whether the series are stationary or not. Table 3 shows the results of the unit root test of both level and first difference on different RRR (including both RRR1 and RRR2) series and inflation rate series. Table 3. Unit root test Level Series First difference ADF P-P RRR1 1.4607** 1.5458** RRR2 -0.0639** Inflation -1.7861** Series ADF P-P RRR1 -5.5916 -9.6549 0.2911** RRR2 -4.0706 -9.7844 -2.1236** Inflation -5.4166 -10.8345 Notes: ADF: Augmented Dickey-Fuller; P-P: Phillips-Perron; ADF and P-P are both test types of 6 Unit root tests. RRR1: reserve requirement ratio of large and complex financial institutions; RRR2: reserve requirement ratio of small and medium financial institutions. ** indicates the null hypothesis that there is a unit root of level series are accepted at the 10% significance level; no superscript means the null hypothesis that there is a unit root of first-differenced series are rejected at the 1% significance level. The unit root test shows that all the level series are significantly outside away from the t-statistic of critical value at the 10% significance level. So we accept the hypothesis that the series have a unit root, which means that all level series are non-stationary at the 10% level of significance. However, we reject the hypothesis that all the first-differenced series have a unit root, which has the same meaning as all the first-differenced series are stationary at the 1% significance level. The unit root test results are consistent with the non-stationary null hypothesis. Therefore we draw a conclusion that all individual series say both inflation series and the reserve requirement series are I(1). 4.4 Johansen Co-integration Test 18 Under the conclusion that all individual series are I(1), we execute the Johansen co-integration tests on all the series then. Table 4 is a summary of the results and presented as follows: Table 4. Johansen Co-integration test Dependent Trace Trace Max-Eigen Max-Eigen variable r=0 r≤1 r=0 r≤1 RRR1 70.4805* 0.9145 69.5660* 0.9145 RRR2 154.9130* 0.0288 154.8842* 0.0288 Notes: RRR1: reserve requirement ratio of large and complex financial institutions; RRR2: reserve requirement ratio of small and medium financial institutions. * indicates the null hypothesis that there is no co-integration relationship between inflation and reserve requirement ratio is rejected at the 5% significance level. Both trace test and max-eigenvalue test indicate one co-integration eqn(s) at the 5% significance level. Trace r=0 represents the null hypothesis that there is no co-integration relationship between inflation and reserve requirement ratio. No matter for RRR1 or RRR2, both the trace statistic and Maximum eigenvalue statistic are significantly larger than the critical value, so we reject the null hypothesis at the 5% significance level, which means there is definitely a co-integration relationship between reserve requirement ratio and inflation. Trace r≤1 denotes the null hypothesis that there is at most one co-integration relationship, both the trace statistic and Maximum eigenvalue statistic lie in acceptance region. However, the statistics are quite significantly different from the critical value of RRR1 and RRR2. The probability is 90.93% for both RRR1 and RRR2, which indicates that there is only one co-integration relationship between RRR and inflation. The co-integration relationship means that there must be a long-term relationship between RRR and inflation. 4.5 Granger Causality Test 19 In order to analyze whether inflation causes RRR in the long-run and the dynamics of RRR changes in response to inflationary pressures or not, we then carry out Granger causality test to determine which variable is the cause and which variable is the result actually. The results of the Granger causality test are shown in Table 5. Table 5. Pair-wise Granger causality test Lag=2 Null hypothesis Observations F-statistic Probability INFLATION does not Granger cause RRR1 147 7.4597 0.0008 RRR1 does not Granger cause INFLATION 147 3.3408 0.0382 INFLATION does not Granger cause RRR2 147 7.5824 0.0007 RRR2 does not Granger cause INFLATION 147 2.6664 0.0730 Lag=13 Null hypothesis Observations F-statistic Probability INFLATION does not Granger cause RRR1 136 1.4603 0.1442 RRR1 does not Granger cause INFLATION 136 2.7249 0.0023 INFLATION does not Granger cause RRR2 136 1.7155 0.0675 RRR2 does not Granger cause INFLATION 136 2.5305 0.0045 Notes: RRR1: reserve requirement ratio of large and complex financial institutions; RRR2: reserve requirement ratio of small and medium financial institutions. The results are quite sensitive to the choice of the lag length. First, we choose a lag length of 2 months based on standard information criteria such as final prediction error, Akaike Information Criterion (AIC), Schwarz Information Criterion (SIC), and Hannan-Quinn Criterion (HQC), the results show that changes in inflation Granger 20 cause changes in RRR, and vice versa. Second, we increase the lag length to 13 months to eliminate persistence (i.e. serial correlation) in the error term of the bivariate Vector Auto Regressions (VARs), the results reflect that changes in RRR Granger cause changes in inflation, and vice versa for SMFI, but for LCFI, changes in inflation does not Granger cause changes in RRR. These findings are consistent with the view that in the long term, changes in reserve requirement ratio pose a delayed effect on inflation. 4.6 Estimated Coefficients of Long-term Relationship Then we run the Ordinary Least Squares (OLS) regressions on RRR and inflation. Table 6 shows the results. Table 6. Long-term relationship yt 0 1 xt t Intercept yt Slop Adj. R 2 Coefficient T-value Coefficient T-value RRR1 8.3393 20.8540 0.8924 6.9549 0.2425 RRR2 7.8872 25.5902 0.8241 8.3328 0.3162 Notes: RRR1: reserve requirement ratio of large and complex financial institutions; RRR2: reserve requirement ratio of small and medium financial institutions. The long-term relationship between RRR1 and inflation can be estimated as follows: RRR1 8.3393 0.8924Inflation (20.8540) (6.9549) R 2 =0.2475 The long-term relationship between RRR2 and inflation can be estimated as follows: 21 RRR2 7.8872 0.8241Inflation (25.5902) R 2 =0.3208 (8.3328) The results show that in the long term, for both LCFI and SMFI, the relationship between RRR and inflation is quite weak. There is no complete long-run adjustment ( 1 1 ). For RRR1, 1% increases in inflation will generate 0.8924% increases in RRR1, only 24.25% predicted values can be matched with actual values. For RRR2, 1% increases in inflation will affect increases in RRR2 by 0.8241%. Inflation has a greater impact on RRR2 comparing with RRR1 and 31.62% predicted values can be matched with actual values. The weak long-term relationship indicates that as a tool of monetary policy, the reserve requirement ratio is rather ineffective in China, which may be attributed to the macro-economic regulatory objective of “maintaining financial currency stability, stimulating economic growth and creating more job opportunities”. 4.7 Symmetric Error Correction Model As the RRR is found to be weakly co-integrated with the inflation rate, the proper short-run dynamics is given by the error-correction model. The results of symmetric short-term adjustment dynamics between the RRR and inflation are reported in Table 7. Table 7. Symmetric adjustment speed: yt 1xt 2 ( yt 1 0 1 xt 1 ) vt 2 1 y Adj. R 2 MAL Coefficient T-value Coefficient T-value RRR1 0.0752 1.7859 0.0028 0.3907 -0.0550 330.2857 RRR2 0.0903 1.9280 -0.0117 -1.1411 -0.0021 77.7521 22 Notes: RRR1 reserve requirement ratio of large and complex financial institutions; RRR2: reserve requirement ratio of small and medium financial institutions. represents first difference operator, t 1 ( yt 1 0 1 xt 1 ) is the extent of disequilibrium at time (t-1). 1 denotes the immediate response of RRR to changes in inflation and 2 captures the error correction adjustment speed. The mean adjustment lag is calculated by (MAL) = (1 1 ) / 2 The short-term dynamics between RRR1 and inflation can be estimated as follows: RRR1,t 0.0752Inflationt 0.0028( RRR1,t 1 8.3393 0.8924Inflationt 1 ) (1.7859) (0.3907) R 2 =-0.0478 The short-term dynamics between RRR2 and inflation can be estimated as follows: RRR2,t 0.0903Inflationt 0.0117( RRR2 ,t 1 7.8872 0.8241Inflationt 1 ) (1.9280) (-1.1411) R 2 =0.0047 The results show that only the estimate coefficient of 1 is found statistically different from zero at the 10% significance level, implying for both LCFI and SMFI, there is a certain degree of response of reserve requirement ratio to changes in inflation. 1% change in inflation will cause 0.0752% change of reserve requirement ratio for LCFI and 0.0903% for SMFI, indicating that changes in inflation has greater influence on changes in reserve requirement ratio for SMFI than that for LCFI. The estimate coefficients of 2 are not significant for both LCFI and SMFI, implying that the extent of disequilibrium at time (t-1) has little effect on reserve requirement ratio and there is no error correction in the adjustment process. 23 Consequently, the mean adjustment lags (MAL) are very large, even it takes the RRRs several years to resume back to the equilibrium levels, indicating that the RRRs are always kept on disequilibrium levels. In a word, the RRRs of both LCFI and SMFI respond to inflation pressures in the short-run. This is consistent with the fact that Chinese government has adjusted reserve requirement ratio frequently according to changes in inflation in recent years. However, the results show that there is no error correction at all for symmetric error correction model. 4.8 Asymmetric Error Correction Model Because the short-term adjustment speed may not be necessarily the same when the RRRs are above their equilibrium levels as when they are below, the error correction models would be sometimes asymmetric. We then apply an asymmetric error-correction model (ECM) instead. The results of asymmetric error correction model are reported in Table 8. Table 8. Asymmetric adjustment speed: yt 1xt 2 ˆt 1 3 (1 )ˆt 1 t 2 1 3 y Coefficient T-value Coefficient T-value Coefficient T-value RRR1 0.0641 1.5206 0.0174 1.6428 -0.0092** -0.9576 RRR2 0.0727 1.5680 0.0088 0.6902 -0.0465* -2.7957 Notes: RRR1 reserve requirement ratio of large and complex financial institutions; RRR2: reserve requirement ratio of small and medium financial institutions. 24 represents first difference operator, ˆt 1 ( yt 1 0 1 xt 1 ) is the extent of disequilibrium at time (t-1). 1 denotes the immediate response of changes in RRR to changes in inflation, 2 reflects the speed of error correction adjustment when the RRRs are above their equilibrium levels and 3 captures the error correction adjustment speed when the RRRs are below their equilibrium levels. ** and * indicate based on the Wald test, there is significant difference in the adjustment speeds when the RRRs are above and below their equilibrium levels at 10% and 1% significance level respectively. The detailed results of the Wald statistical test can be obtained from the author. The asymmetric short-term dynamics between RRR1 and inflation can be estimated as follows: RRR1,t 0.0641Inflationt 0.0174 1,t 1 0.0092(1 ) 1,t 1 (1.5206) (1.6428) (-0.9576) R 2 =-0.0236 The asymmetric short-term dynamics between RRR2 and inflation can be estimated as follows: RRR2,t 0.0727Inflationt 0.0088 2,t 1 0.0465(1 ) 2,t 1 (1.5680) (0.6902) (-2.7957) R 2 =0.0499 The results show that for both LCFI and SMFI, inflation and extent disequilibrium of time (t-1) when reserve requirement ratios above their equilibrium levels have little effect on changes in the RRRs in the short term due to the insignificant statistics of parameters. 25 For LCFI, the extent disequilibrium of time (t-1) when RRRs are below their equilibrium levels is not significant. However, this is not the case for SMFI. The negative estimate coefficient of 3 is very significant at 1% significance level, indicating that the RRRs of SMFI tend to move upwards when RRRs are below their equilibrium levels. This is consistent with the fact that the reserve requirement ratios of SMFI are usually kept above their equilibrium levels. Based on Wald test result, for both LCFI and SMFI the asymmetric adjustment speeds 2 is more quickly than 3 and both of them are quite different from each other at 10% and 1% significance level respectively. No matter whether the RRRs are above or below their equilibrium levels, RRR2 are always smaller than or equal to RRR1. One explanation for the asymmetric adjustment speed between LCFI and SMFI is that a relatively low reserve requirement environment is easy for SMFI to play an active role in economic activities in competition with LCFI. However, the results show that there is no asymmetric error correction either. 4.9 Mean Adjustment Lags The adjustment above and below the equilibrium levels can either be symmetric or asymmetric, so the adjustment speed in different models and for different size financial institutions is further tested with the method of mean adjustment lags (MAL) for both situations. The results of mean adjustment lags in months are summarized in Table 9. 26 Table 9. Mean adjustment lags in months Symmetric model Asymmetric model MAL MAL+ MAL- LCFI 330.2875 53.7874 101.7282 SMFI 77.7521 105.3750 19.9419 Notes: LCFI: large and complex financial institutions; SMFI: small and medium financial institutions. MAL (1 1 ) / 2 is the mean adjustment lag for the symmetric ECM. Under the asymmetric ECM, MAL (1 1 ) / 2 denotes the mean adjustment lag when the RRRs are above their equilibrium levels and MAL (1 1 ) / 3 reflects the mean adjustment lag when the RRRs are below their equilibrium levels. The results in Table 9 indicate that the short-run adjustment speed differs across financial institutions. The symmetric mean adjustment lag of large and complex financial institutions is much larger than that of small and medium financial institutions. Our result on the dynamics of asymmetric model shows that for LCFI, the RRRs change more quickly when they are above than below their equilibrium levels, but for SMFI, the situations are completely different. This indicates that government tends to keep reserve requirement ratios of LMFI below their equilibrium levels but to keep that of SMFI above their equilibrium levels in order to balance the gaps between RRR1 and RRR2, which is good for economic growth in the long run. The large lags show that both the long-term relationship between RRR and inflation and short-term dynamics between changes in RRR and changes in inflation are quite weak, we need to use other models or add other factors into the model in order to explain the mechanism of setting RRR more reasonably. 27 CHAPTER 5: CONCLUTIONS Many developed countries focus their paramount concern on controlling inflation in order to maintain sustainable economic growth in the future. However, because of the immature nature of Chinese economy environment, this is not the case in China. The primary objective of monetary policy is to maintain the stability of the domestic currency-Renminbi, thus further promoting sustainable economic growth. Our results show that there is a quite weak long-term relationship between reserve requirement ratio and inflation, reflecting the fact that as a tool of monetary policy, reserve requirement ratio is rather ineffective in China. This may be attributed to the macro-economic regulatory objective of keeping financial currency at a relative stable level, stimulating economic growth and creating more job opportunities. In the short-run, the reserve requirement ratios of both large and complex financial institutions and small and medium financial institutions respond somewhat to inflation pressures, and changes inflation has a greater influence on changes in reserve requirement ratio for small and medium financial institutions than that for large and complex financial institutions. However, the extent of disequilibrium at time (t-1) has little effect on reserve requirement ratio. The reserve requirement ratios of small and medium financial institutions tend to move upwards when they are below the equilibrium levels. Government tends to keep reserve requirement ratios of large and complex financial institutions below their equilibrium levels but to keep that of small and medium financial institutions above their equilibrium levels in order to balance the gaps between them, which is good for economic growth in the long run. However, there is no error correction at all for both symmetric and asymmetric error correction model. 28 REFERENCES Chong, Beng Soon, Liu, Ming-Hua and Shrestha, Keshab, 2006. “Monetary transmission via the administered interest rates channel,” Journal of Banking & Finance, Elsevier, vol. 30(5), pages 1467-1484, May. Cottarelli, C. and Kourelis, A. (1994) Financial structure, bank lending rates, and the transmission mechanism of monetary policy, IMF Staff Papers, 41, 587-623 Espinosa-Vega Marco A. & Alessandro Rebucci, (2003). “Retail Bank Interest Rate Pass-Through: Is Chile Atypical?,” Working Papers Central Bank of Chile 221, Central Bank of Chile. Hannan, T. and A. Berger (1991). “The Rigidity of Prices: Evidence from the Banking Industry”. American Economic Review, September 1991, 81: 938-945. Heffernan, S.A., 1997. Modelling British interest rate adjustment: An error correction approach Economica 64, 211-231 Hofmann, B. and Mizen, P(2004) Interest rate pass-through and monetary transmission: evidence from individual financial institutions’ retail rates, Economica, 71, 99-123 Kashyap, A.K., Stein, J., 2000. What do a million observations on banks say about the transmission of monetary policy. American Economic Review 86, 310-314 Liu, Ming-Hua & Margaritis, Dimitri & Tourani-Rad, Alireza, (2008). “Monetary policy transparency and pass-through of retail interest rates,” Journal of Banking & Finance, Elsevier, vol. 32(4), pages 501-511, April. 29 Liu M.-H.;Margaritis D.and Tourani-Rad A.(2009) Monetary policy and interest rate rigidity in China , Applied Financial Economics, 19:8, 647-657 Sarno, L., Thornton, D.L., Wong, R.W.Y., 1994. Banking, Finance and Monetary Policy in Singapore. McGraw-Hill, Singapore. Scholnick, B. (1996). “Asymmetric adjustment of commercial bank interest rates: evidence from Malaysia and Singapore”. Journal of International Money and Finance, Vol. 15, pp. 485-496, 1996. Wang, S. and Handa, J. (2007) Monetary policy rules under a fixed exchange rate regime: empirical evidence from China , Applied Financial Economics, 17,541-50 Wang, Y. and Ma, M. (2001) Reforming the transmission mechanism of monetary policy in China, China and World Economy, 6, 34-9 Xie, P. (2004) China’s monetary policy, Working Paper No. 217, Stanford Center for International Development, Stanford University. 30