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 xt 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  1xt   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  1xt  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  0.0752Inflationt  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  0.0903Inflationt  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  1xt   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  0.0641Inflationt  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  0.0727Inflationt  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
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