Testing Unit Roots Allowing For Two Breaks Economics Essay

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It is now widely accepted that inflation has a negative effect on economic growth and social stability. China has experienced varying degree of fluctuated inflation after the financial crisis. The aim of this dissertation is to study during the financial crisis how the inflation rate fluctuates, what factors are responsible for this fluctuation, whether the global financial crisis has a significant impact on the inflation in China, and reflect on some of the implications that recent economic experience has for monetary and financial stability policies.

There are two key questions which are addressed: Is the effect of the financial crisis on China's inflation temporary or permanent? What factors contribute most to the fluctuation of China's inflation - the domestic prices fluctuation caused by the financial crisis or the monetary policy made by Chinese government?

Knowing Chinese financial market's less exposure to the sub-prime U.S. mortgages, I predict that the impact of financial crisis on China's inflation is temporary, and the high inflation rate is mainly caused by the monetary policy of Chinese government.

The period of inflation data analyzed is from January 2007 to December 2011. In order to measure unit root and structural break, this paper use three different tests to analyze the existence of structural breaks and find weak evidence that there is a structural break in the inflation process. Hence this paper concludes that the impact from the financial crisis on the high inflation rate in china is smaller compared with the impact from the policy made by Chinese government.

Introduction

For the past three decades, China's boost economic growth keeps an average 10% annual GDP growth rate in real terms since 1978. Compared with 4 percent for all developing countries, China has gained tremendous international attention and praise. China has not only enjoyed one of the world's fastest growing economies but also been a major contributor to world economic growth. Although China's per capita GDP is still modest ($5,445 in 2011), it had become the world's second biggest economy by 2011. Moreover, China's foreign exchange reserves, which now exceed $3 trillion, are the largest in the world.

To manage the float currency policy, China's placing numerous restrictions on capital flows outflows and limiting the ability of Chinese citizens and many firms to invest their savings overseas seemed to keep the Chinese private sector firms and individual investors from exposures to the toxic assets in the developed world. In addition, China's strong fiscal position, highly regulated domestic and recently recapitalized banking sector, large foreign reserves, and low short-term debt helped the country mitigate the external shock. However, this did not successfully shield China from the initial global financial turmoil of 2008 and the global financial crisis in 2007 threatened to significantly slow China's economy. It is reported that several Chinese industries have been suffered from great loss and millions of workers to have been laid off. In 2008 the growth of Chinese economy fell to 6.8 percent in the fourth quarter. Moreover, the growth rate in the first quarter of 2009 fell to 6.1 percent, which is the slowest quarterly growth reported in 10 years. In the second quarter in 2012, China's annual economic growth slowed to 7.6 percent, which is a lowest rate below 8 percent since the fourth quarter of 2009. Compared with the number 14.2 percent in 2007, we can see that the 2007 global financial crisis did diminish Chinese economy in a quick way. Given China's heavy reliance on trade and foreign direct investment (FDI), its economic growth, particularly the export sector would be sharply injured, for the impact has been mainly through the trade channel, and not so much through private capital flows and the financial sector.

At the same time, inflationary pressure, which is also a particular concern in fast-growing economies across Asian region, appeared to threaten the Chinese economic stability. China's economy was already slowing down before the crisis hit, for the government was making efforts to slow the inflation rate, that policy-makers believe that high inflation and volatile inflation are both detrimental to economic growth (Judson and Orphanides, 2002). In fact, the inflation rate in China being surprisingly moderate over the past decades contributes a lot to China's continued boost economic growth. However, after the financial crisis shocked, the inflation rate in China has been fluctuated and hit its highest level since 1995 of 8.7 percent in February 2008.

Some economists pointed out that China's economy today looks much as it did before during the inflationary catastrophes when the economy experienced an intricate development. The first high inflationary period is from 1988 to 1989, when the inflation rate reached a high level of 18.5 percent in 1988. It was followed by the commencement of economic reforms which promoted the growth of both M2 and domestic credit in late 1982 (with the rate of 40 percent in 1985 and 50 percent in 1986 respectively). Then the liberalization and deregulation of prices in 1987 further led to the overheating inflation which occurred in 1988. To cope with this situation and curb the inflation, the government tightened money and credit supply and cut the fixed investment. Though successfully the inflation rate went down, it turned out that the macro policy was over tightening, the economic growth rate fell below 5 percent and the output kept decreasing in the next three years. The second inflationary period is from 1993 to 1996. Concentrated on developing economic growth and encouraging investments, the Chinese government loosed credit control that the money supply grew at a rate of 50 percent in 1994, thus leading the inflation rate to peak at an extraordinary point at 27.7 percent.

Though the inflation rate in China now is less responsive than it used to be to external shocks (Zhang and Clovis, 2010), and did not reach such high levels as during the inflationary catastrophes , high inflation may appear again if the government were less cautious in maintaining a low inflation level. The launch of expansionary monetary and fiscal policies, like many other countries, aiming to offset the fast slowdown in economic activity worldwide, indicates that the financial crisis might be a potential stimulus factor to the future high inflation in China. To keep the economy growth stability, the inflation needs to be controlled by vigilance and effective response to avoid hardship and social unrest.

Since fighting the rising prices and keeping society stable have been a top priority for China, the country's government has taken a number of policies to respond. Firstly, the Chinese government launched a two-year 4 trillion yuan ($635 billion) package (equivalent to 13.3 percent of China's 2008 GDP) in November 2008 to boost Chinese demand for imports and stimulus the domestic economy. Secondly, the Chinese central bank has been raising interest rates to rein the rising prices and take control of the inflation volatility. To fight this extreme inflation increase and boost bank lending, the People's Bank of China announced in October 2010 the first interest rate increase in nearly 3 years. In December 2010, the central bank raised the benchmark interest rate by a quarter of a percentage point; this move increased the 1-year lending rate to 5.81 percent and the 1-year deposit rate to 2.75 percent. In addition to cutting interest rates, China has implemented a number of steps to increase consumer spending, restructure and subsidize certain industries. The government has also announced price control guidelines and taken numerous measures to reign in its monetary policy and boost the supply of key goods.

On the other hand, the issue of inflation in China is highly important not only because of its effect on the domestic monetary policy and decision making for both consumption and investment, but also because of its spillover effect in the global economy. According to the International Monetary Fund (IMF), China is now the largest exporter of goods, with 9.6 percent of the global share, followed by Germany, the United States and Japan. Thus, being a major economic power, a Chinese economic recovery could also have significant global implications.

The instable inflation fluctuation in China after the financial crisis implies that the global financial crisis may have a significant impact on the inflation. Although there have been many studies focusing on the cause and effect of the financial crisis, there are few which focused on the direct effects on Mainland China's inflation rate, despite its importance to the economic growth. Therefore this dissertation aims to update and add something new to current research. The objective of the paper is to use the recent development of unit root hypotheses in the presence of structural change at the unknown time of the break to find whether the impact of the financial crisis is temporary. Then the vector auto regression (VAR) model would be used to identify the response of consumer price index (CPI) to the impulses of different variables including money supply, exchange rate, interest rate and the prices of oil.

The paper is structured as follows. The next section provides the literature review. In section 3, we discusses the data and methodology, demonstrates the application of the techniques presented in the previous sections, then we display the results of the empirical study of testing the existence of structural breaks and VAR model. Finally, in section 4, some concluding remarks would be presented.

Literature Review

Inflation refers to a general process that prices continue to rise or the process that the value of currency keeps decreasing (Laidler and Parkin, 1975). There are many theories of the cause of inflation. The mainstream economic opinion suggests that inflation is caused by combined effect of the money supply with output and interest rates. The "monetarists" argue that monetary effects are mainly responsible for the occurrence of inflation and inflation occurs when the money supply rises faster than the national income growth. While the "Keynesians" insist that the interaction of money, output and interest dominate all other factors. In terms of the Austrian school of economics, they consider the increase of money supply as the main reason for inflation.

2.1 Is the effect of the financial crisis on China's inflation temporary or permanent?

The find out whether the global financial crisis has a significant and permanent impact on the inflation of China, the tests of structural breaks can be used to determine whether the inflation has structural changes during and after the global financial crisis, that is, whether the financial crisis has temporary or permanent effect on the inflation. Different quantitative methods for measuring structural breaks will be applied in order to compare any differences.

A structural break may be the change in the time series as a result of some unique economic event reflecting institutional, legislative or technical change which will occur in economic policies or external large economic shocks, such as the Asian crisis in 1997. Structural breaks can occur in the inflation process and a number of researchers have set up models about it. When dealing with time series, most macroeconomic variables are non-stationary which suggests that it might be necessary to isolate some unique economic events, for a structural break can have a permanent affect in the pattern of the time series (Perman and Byrne, 2006). Caskey (1985) examines a linear model with Bayesian learning, and Evans and Wachtel (1993) investigate the possibility that inflation shifts between a stationary and unit root process.

Structural breaks can be determined endogenously or exogenously and they can also be multiple -- that is more than one structural break can occur in the time series. So the question of this thesis becomes that if any structural break can be identified in the case of Chinese fluctuated inflation during the financial crisis.

Previous literature has shown that a number of theoretical models have been used to observe and measure the existence of structural break. When detecting structural changes, there are usually three types of tests involved: firstly the traditional unit root tests; then the basic type of structural change with one-time regime shift model of unknown timing; and finally the tests for unit roots allowing for two breaks. Hence, the theoretical framework used in this paper is different quantitative methods for structural breaks in time series. Different tests for both exogenously and endogenously determined breaks are performed in order to compare any differences.

2.1.1 The traditional unit root tests

Firstly, the traditional unit roots test (the ADF test) is performed to test for unit roots, which is one way to detect non-stationarity of the time series. The rejection of the null indicates the time series is stationary while acceptance of the null hypothesis implies that the series is non-stationary.

However, the test tends to have low power in practice that rejection of the null hypothesis does not always mean that the time series is non-stationary. For the traditional view of the unit root hypothesis indicates that current shocks only have a temporary effect in the series, which means that the long-run level of macroeconomics will not be affected by such shocks. Nelson and Plosser (1982) propose that the random shocks can have permanent effects on the long-run movement and the fluctuations are not transitory. The ADF unit roots test were also challenged by Perron (1989), who argues that if not taking into consideration of a possible existing break, it could lead to rejection of a false unit root null hypothesis. Perron suggests that, "Most macroeconomic time series are not characterized by the presence of a unit root. Fluctuations are indeed stationary around a deterministic trend function. The only 'shocks' which have had persistent effects are the 1929 crash and the 1973 oil price shock" (1989, pp.1361).

2.1.2 Unit root tests with one structural break

To improve the model and correct this type of failure, Perron suggests an ADF test allowing for a single exogenous (known) or exogenous structural break according to the underlying asymptotic distribution theory (Glynn et al., 2007). He sets up dummy variables to account for the known or exogenous structural break and modified the Dickey-Fuller (DF) unit root tests. This unit root test allows for a break under both the null and alternative hypothesis by fixing the break point of the trend function and choosing the data independently.

To estimate the unit roots, Perron developes three equations considering the existence of three different kinds of structural breaks: a 'crash' model which allows for a break in the intercept; a 'changing growth' model which allows for a break in the slope; and lastly one that allows for both a break in the intercept and a break in the slope, which means changes occur in both the intercept and the slope of the series simultaneously.

However, the assumption of the known break date was criticized by Christiano (1992) who argues that this approach invalidates the endogenously determining the break point. Since then, several studies have developed using different methodologies to reduce the bias in the traditional unit root tests and determine the most likely location of when the break happens, which include Banerjee, Lumisdaine and Stock (1992), Zivot and Andrews (1992), Perron and Vogelsang (1992), Perron (1997) and Lumsdaine and Papell (1998).

To test the stationarity of the variables with one structural break, the Zivot and Andrews (1992) endogenous structural break test was employed. This test utilizes different dummy variables to identify each possible break point adopting a sequential test and select the break date where the t-statistic from the ADF unit roots test is at a minimum (most negative). As a result, a break date can be chosen where the evidence is least favorable for the unit root null.

Allowing for two different forms of structural break, Perron and Vogelsang (1992) and Perron (1997) proposes a class of test statistics and developed the Additive Outlier (AO) and Innovational Outlier (IO) models. The AO model estimates a sudden change in mean while the IO model allows for more gradual changes. Perron and Vogelsang argue that these tests are based on the minimal value of t statistics on the sum of the autoregressive coefficients over all possible breakpoints in the appropriate auto regression and apply these two models for non-trending data. While Perron (1997, pp. 356), argues that "if one can still reject the unit root hypothesis under such a scenario it must be the case it would be rejected under a less stringent assumption" and modifies them for use with trending data.

To this point we know that applying the procedure for testing the unit root hypothesis allowing for the possible presence of the structural break has several advantages. First, it prevents yielding a test result from becoming biased towards unit root. Second, it can identify the exact time when the possible break occurred. By identifying the possible presence of structural break, it would provide valuable information about whether a structural break on a certain variable is associated with a particular government policy, economic crises, war, regime shifts or other factors. However, some authors claim that there is a trade-off between the power of the test and the amount of information included with respect to the choice of break point and the tests can only capture the single most significant break in the series. This raised the question of what happens if the series includes multiple breaks.

2.1.3 Unit root tests with multiple structural breaks

Lumsdaine and Papell (1997) argue that when more than one break exists, considering only one endogenous break is insufficient and would lead to a loss of information. Hence, they extend the Zivot and Andrews (1992) model, allowing for two structural breaks under the alternative hypothesis of the unit root test and breaks in both level and trend. Also, they argue that this unit roots test is more powerful that those which only allow for a single break.

Clemente, Montañés and Reyes (1998) also considered multiple breaks and extended their approach on Perron and Vogelsang (1992) by allowing for two breaks within the observed history of a time series. This method employs either the AO model which captures a sudden change in a series or the IO model which allows for a gradual shift in the mean of the series.

This thesis continues to examine whether there are structural breaks in the Chinese inflation fluctuation during the global financial crisis by firstly applying the traditional unit root tests not allowing for any breaks, then performing the Zivot and Andrews test which assumes one endogenous break, and lastly using the Clemente, Montañés and Reyes test for two structural breaks purpose.

2.2 What factors contribute most to the fluctuation of China's inflation?

Whether the monetary policy variables have significant impact on the fluctuated inflation in this crisis has several important implications to the design of the monetary policy when faced crises. For policy-makers in both developed and developing countries have often tended to trade short-run price instability for output gain (Judson and Orphanides, 2002), that the fluctuation of the inflation might be the result of the Chinese government's effort to stimulate the growth of economy.

There is a lively debate about the future of monetary policy and its relation to financial stability. Some blame loose monetary policy for laying the foundation for the crisis. Huang (2010) claimed that excess liquidity, output gap, housing prices and stock prices positively affect inflation. While Ouyang (2011) thought that the loose monetary policy should withdraw from practice, but the withdrawal speed should not be too fast and the operation of monetary policy should be flexible. Moreover, Woo (2011) argued that macro-stimulus tools will cause inflation in the medium run and weaken the growth fundamentals in the long run.

After testing the existence of structural breaks, this paper works at the factors that mainly distributed to this fluctuation -- is it because of the impact of the global financial crisis or the effect of the 4 trillion yuan ($586 billion) stimulus package implemented by the Chinese government that the CPI index fluctuated? To exam the pass-through effects of the global financial crisis on the inflation, the statistical vector autoregression (VAR) estimation of Sims (1980) and Johansen-Juselius (1990)allowing for bi-direction effects is used. This method takes into account the feedbacks between the variables included in the analysis (e.g. Blanchard and Galí, 2007; Kilian, 2007, 2008) and also policy reactions to shocks.

VAR models can be estimated to provide evidence on how macroeconomic variables response to exogenous impulses. By determining which of the variables have the most significant impact on the volatility of the inflation would help governments make decisions about what policies to choose in order to control the inflation rate and keep the economic grow healthily.

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Empirical Studies

This study uses a time-series data set, which covers monthly inflation rate and 5 macroeconomic indicators from January 2007 to December 2011. The most well known measures of inflation are the consumer price index (CPI) which measures consumer prices, and the GDP deflator, which measures inflation in the whole of the domestic economy. Here we choose CPI to measure the inflation degree which includes both imported and, more generally, tradable goods and non-tradable goods and services.

Data on the CPI are obtained from the National Bureau of Statistics of China. Data for the 60 monthly macroeconomic variables used in the data-rich set include money supply, interest rates, exchange rates, and price of oil. The data source for all the variables are IMF, National Bureau of Statistics of China, the Word Bank and the Bloomberg Database. To obtain a stationary series, each macroeconomic indicator is transformed into the difference of the logarithm when possible, implying the growth rate of the variable. All transformed variables are verified to be stationary.

This paper will focus on the period from the start of the crisis through October 2008 when market conditions deteriorated precipitously and rapidly. The CPI have been continuously declined from the peaks (more than 8%) in the first half of 2008 to almost zero in the end of the year and began to be negative in the early 2009.

To examine the CPI index in order to determine whether there are any structural breaks in the time series, the theoretical framework used in this thesis is different tests for unit root and structural breaks in time series.

To test if there was a structural break, first the traditional Dickey-Fuller and the ADF test to examine whether the series contains a unit root or not is performed. Then the Zivot-Andrews unit root test with one endogenously determined structural break is used to compensate the possible bias to reject a false unit root null hypothesis. Finally a test proposed by Clemente, Montañés and Reyes for two breaks is performed to explore if it is possible for the series to exhibit more than one structural break.

3.1 Traditional Unit Root Tests

First, the Augmented Dickey-Fuller test was used to test for unit root without allowing for any structural breaks. The variables tested are the CPI data under the period 2007 to 2011. Table 1 shows the results from the tests.

Table 1. ADF Unit Root Test

T statistics

P-value

1lag

Result

CPI

-0.940

0.7744

0.8999

Unit Root

First Difference of CPI

-5.612

0.0000

0.0652

No Unit Root

The 1%, 5% and 10% critical value are -3.567, -2.923 and -2.596 respectively.

The ADF test for unit root shows that the CPI series are non-stationary and taking the first difference of the series makes the series stationary.

3.2 Testing Unit Roots Allowing for Endogenous Breaks

The Zivot-Andrews test was used to test for unit root allowing for one endogenously determined structural break. The variables tested are the CPI data under the period 2007 to 2011. Table 2 shows the results from the tests.

Table 2. Zivot-Andrews Unit Root Test Allowing for One Break

Break

in intercept

in trend

in both intercept and trend

Lags included*

2

2

2

Minimum t-statistics

-5.352

-1.945

-3.376

Time

Dec. 2009

May 2008

Dec. 2009

1% Critical Value

-5.43

-4.93

-5.57

5% Critical Value

-4.80

-4.42

-5.08

*Lag selection via TTest.

When using the Zivot-Andrews test the break date is chosen where the t-statistics for α is most significant, this is where the t-statistic from the ADF test of unit root is at a minimum. This is the break date where there is strongest evidence against the null hypothesis of unit root. The null hypothesis of unit root cannot be rejected in the test of break exists in trend or both in intercept and trend, while the t-statistics of break exists in intercept at 5% level critical value is significant.

Figure 1. Zivot-Andrews unit root test

1.png

3.3 Testing Unit Roots Allowing for Two Breaks

The Clemente, Montañés and Reyes test was used to test for unit root allowing for two structural breaks. The variables tested are the CPI data under the period 2007 to 2011. Table 3 shows the results from the tests.

Table 3. Clemente, Montañés and Reyes test with double mean shifts

Variable

Additive Outlier(AO)

Innovational Outlier(IO)

CPI

Min t*

Optimal Breakpoints

Min t*

Optimal Breakpoints

-2.062

Dec. 2008 and Dec. 2009

-4.880

Oct. 2008 and Oct. 2009

* Min t is the minimum t-statistics calculated. 5% critical value for two breaks: -5.490.

In the AO model changes are assumed to take place rapidly allowing for a break in the slope. In the IO model changes are assumed to take place gradually and allows for a break in both the intercept and the slope. Despite the breaks in the CPI the null hypothesis of unit root cannot be rejected in either the AO or IO model. The t-statistics for the dummy variables included in the models and the statistics for CPI shows that the IO model is more significant in explaining the price series than the AO model, which indicates that the series is more likely to exhibit structural breaks that take place gradually rather than rapidly.

Figure 2. Clemente, Montañés and Reyes test with double mean shifts

5.png

3.jpg

Comparison

In the first stage tests for unit root without allowing for any structural breaks was used. Perron (1989) argued that in the unit root tests, the results tend to be biased if it did not account for structural breaks which indicate permanent changes in the pattern of time series. However, allowing for both one and two breaks in the Zivot-Andrews and Clemente et al. test the results indicates the same. The concluding remark is therefore that the CPI series exhibits unit root even structural breaks are included.

Table4. Summary of Unit Root and Structural Breaks Tests

ADF test

Zivot-Andrews

Clemente et al. AO model

Clemente et al. IO model

Unit Root

Yes

Yes

Yes

Yes

Break Points

Dec. 2009

Dec. 2008 and Dec. 2009

Oct. 2008 and Oct. 2009

A possible explanation of the structural break is the sudden increase of money supply during 2008-2010. The most important policy response in this period is the stimulus package of 4 trillion Yuan for 2009-2010 announced in November 2008, for stimulating the domestic demand through enhancing the public expenditure. However, only the t statistics of break exists in intercept at 5% level critical value in the Zivot-Andrews test is significant. While the t statistics of break exists in intercept at 1% level critical value is insignificant, which means that the tests reject the existence of a structural break at the 1% level. At the mean time, results from the Clemente, Montañés and Reyes test indicates that there is no structural break in the inflation fluctuated period.

Hence there is weak evidence in accepting the null hypothesis and we cannot argue there is a structural break in the Chinese inflation between 2007 and 2011.

To confirm that it was the money supply led to the fluctuation of inflation, more analyses need to be conducted. Many studies (e.g. Stephen, 1995) considered real variables, such as exchange rate, M1 and M2 (money supply), interest rate, the prices of oil, are correlated with inflation at horizons of 1 or 2 years, and from figure 3 we can see that these variables did vary in different degrees during the financial crisis. To allow the reverse causality from of the money supply changes on the domestic price, and in order to take into account of bi-directional effects between other macroeconomic factors, a VAR analysis by employing cointegration and Granger causality techniques with five variables is used. With these variables affecting each other, here we set up the following VAR model with the vector of five endogenous variables:

,

where denotes the consumer price index; all the five variables are expressed in log, the natural log of money supply M1; the natural log of money supply M2 (which is a key economic indicator often used by economists when try to explain different economic monetary situations); the natural log of interest rate; the natural log of exchange rate; the natural log of oil price. Δ represents the first difference operator, for these variables appear to be non-stationary in level but stationary in first-differences for all countries. As will be discussed below, we take the first-difference of all variables to ensure the stationarity of variables. The VAR approach allows the interaction between variables and enables us to identify monetary policy shocks controlling for other factors. If indeed the monetary factor was dominant, then we will confidently determine the source of fluctuated inflation as a result of the monetary policy.

Figure 3. Data used for the VAR analysis, January 2007 - December 2011

CPI 2. Interest rate

11.png14.png

3. Exchange rate 4. Oil price

15.png16.png

5. Money supply (M1, M2)

13.png

3.4 The VAR Analysis

The VAR analysis of CPI with M1 and M2, exchange rate and interest rate, price of oil:

M1

M2

r

i

insignificant

0.000(L1)

insignificant

0.043(L2)

M2

r

i

M1

insignificant

0.003(L1)

0.003(L2)

insignificant

insignificant

M1

r

i

M2

0.020(L2)

0.001(L2)

insignificant

insignificant

M1

M2

i

r

insignificant

0.030(L2)

0.000(L1)

0.004(L2)

M1

M2

r

i

0.004(L1)

insignificant

0.001(L2)

0.026(L1)

Note: Critical value is 5%; L1, L2 means Lag1 and Lag2.

The VAR analysis above shows that the p-value of M1 and M2, exchange rate and interest rate except oil price are statistically significant, while the p-value of oil price is not significant, indicating that the effect of changes in the price of oil is not that obvious as the other four variables.

After identified the macroeconomic variables that have significant effects on CPI, this paper tends to measure specifically how much the effects are. Here the impulse response function (IRF) is used to analyze the reduced form VAR model. The IRF measures the effect of a shock to an endogenous variable on itself or on other endogenous variables, and here we use this method to measure the effect of money supply, interest rate and exchange rate have on CPI, in order to figure out which variable has the most significant effect on CPI and is mainly responsible for the inflation's fluctuation.

Figure 4. Impulse Response of CPI

1. Response to M1 2. Response to M2

i 2.pngi 1.png

3. Response to interest rate 4. Response to exchange rate

i 3.pngi 4.png

From the figure above we can see that in reaction to the shock of M2, the response of CPI exhibits a larger varying degree compared with other three factors, and the changes of CPI tend to be diminishing quickly when the impact of M2 disappeared. The maximum value of CPI responses to a one percent M2 shock is bigger than 2, while the maximum value of CPI responses to M1, interest rate and exchange rate is less than 1, which indicates M2 affects CPI the most and we can confirm that it was mainly the money supply (the stimulus package of 4 trillion Yuan) led to the fluctuation of inflation. This result coincides with the conclusion of the research conducted by Fang and Wu (2009), which shows that the increase of money supply is the main reason why inflation occurred in China, while the external shocks by the global financial crisis is secondary.

Concluding Remarks

In this paper, two key questions are addressed: Is the effect of the financial crisis on China's inflation temporary or permanent? What factors contribute most to the fluctuation of China's inflation - the domestic prices fluctuation caused by the financial crisis or the monetary policy made by Chinese government? To solve these questions, different unit root tests allowing for structural breaks were conducted and the pass-through effects of the macroeconomic factors on the consumer prices for China were analyzed. The structural breaks tests and VAR analysis on CPI has revealed several new important facts.

The Chinese 4 trillion yuan stimulus package has sparked a wave of controversies that although there is little doubt that the massive fiscal stimulus will largely compensate the significant insufficient in external demand, the current growth pattern in China will be increasingly unsustainable in the long run. Some argue that the package is unnecessary and risky in the long term, while some approve of the program and support the government to do more and stimulate the economy. In this case, what main factors should the government take into consideration when making policy decisions?

Results of the empirical studies here showed that the unit root tests did not show enough evidence for the existence of structural breaks; the global financial crisis does not have a significant impact on the inflation volatility by affecting the interest rate, exchange rate and price of oil; and the degree of domestic price response to M2 is larger than to other macroeconomic variables, which indicates that prices level is quite sensitive to the monetary policy in China. The finding above suggests that the fluctuation of Chinese inflation this time is mainly caused by the expansionary monetary policy not the global financial crisis. Hence, to curb the high inflation and avoid social unrest, we should turn to the improvements of government's policy decision. In addition, Mishkin (2007) claims that better monetary policy can cut down inflation persistence for it lowers the inflation expectation, while Zhang and Clovis (2010) confirms this theory by examining the inflation rate in China since the late 1990s, when the People's Bank of China not only successfully kept the inflation low but also anchored long term inflation expectation. Hence we can see how much is the inflation influenced by the monetary policy and the policy makers must be prudent when controlling the speed of the inflation, especially given the deceleration of economic growth now, the government should be vigilant about whether more economic spur is needed.

In terms of options for policy after the financial crisis, policy maker must ensure to keep in mind that the key objective of monetary policy in the longer term should be shifted to financial stability so as to return the economy to a sustainable path. In particular, as the results of this paper revealed, the massive monetary expansion could be perceived as creating an inflation risk in the long run.

Appendix : Raw Data

Date

CPI

m1

m2

Interest Rate

Oil Price

Exchange Rate

31/12/2011

4.1

7.9

13.62

3.25

117.51

6.3009

30/11/2011

4.2

7.8

12.72

3.25

119.76

6.3482

31/10/2011

5.5

8.42

12.88

3.25

118.61

6.3233

30/09/2011

6.1

8.85

13.04

3.25

115.02

6.3549

31/08/2011

6.2

11.2

13.56

3.25

123.41

6.3867

31/07/2011

6.5

11.56

14.65

3.25

125.23

6.4442

30/06/2011

6.4

13.05

15.85

3.25

119.74

6.4716

31/05/2011

5.5

12.68

15.07

3.25

124.56

6.4845

30/04/2011

5.3

12.89

15.34

3.25

131.83

6.499

31/03/2011

5.4

15.01

16.63

3.25

122.62

6.5564

28/02/2011

4.9

14.49

15.71

3.25

116.68

6.5752

31/01/2011

4.9

13.55

17.2

3.25

101.86

6.5891

31/12/2010

4.6

21.19

19.72

3.25

92.28

6.6227

30/11/2010

5.1

22.07

19.45

2.79

93.14

6.6762

31/10/2010

4.4

22.1

19.3

2.79

90.23

6.6908

30/09/2010

3.6

20.87

18.96

2.79

85.27

6.7011

31/08/2010

3.5

21.93

19.21

2.79

79.68

6.8105

31/07/2010

3.3

22.86

17.61

2.79

80.39

6.775

30/06/2010

2.9

24.56

18.46

2.79

80.4

6.7909

31/05/2010

3.1

29.93

20.99

2.79

75.73

6.828

30/04/2010

2.8

31.25

21.47

2.79

88.29

6.8263

31/03/2010

2.4

29.94

22.49

2.79

84.43

6.8263

28/02/2010

2.7

34.99

25.53

2.79

79.45

6.8269

31/01/2010

1.5

38.96

26.1

2.79

76.58

6.827

31/12/2009

1.9

32.35

27.7

2.79

82.43

6.8282

30/11/2009

0.6

36.63

29.7

2.79

79.22

6.8272

31/10/2009

-0.5

32.03

29.5

2.79

80.13

6.8281

30/09/2009

-0.8

29.51

29.3

2.79

68.89

6.829

31/08/2009

-1.2

27.72

28.5

2.79

74.33

6.8312

31/07/2009

-1.8

26.4

28.4

2.79

73.93

6.8323

30/06/2009

-1.7

24.8

28.5

2.79

74.16

6.8319

31/05/2009

-1.4

18.7

25.7

2.79

66.89

6.8324

30/04/2009

-1.5

17.5

26

2.79

52.86

6.825

31/03/2009

-1.2

17

25.5

2.79

51.75

6.8359

28/02/2009

-1.6

10.6

20.4

2.79

50.24

6.8379

31/01/2009

1

6.7

18.8

2.79

46.17

6.838

31/12/2008

1.2

9.1

17.8

2.79

40.84

6.8346

30/11/2008

2.4

6.8

14.8

3.06

49.53

6.8349

31/10/2008

4

8.9

15

4.14

60.16

6.8258

30/09/2008

4.6

9.4

15.3

4.14

96.3

6.8183

31/08/2008

4.9

11.5

16

4.14

120.57

6.8345

31/07/2008

6.3

14

16.4

4.14

134.41

6.8388

30/06/2008

7.1

14.2

17.4

4.14

148.17

6.8591

31/05/2008

7.7

17.9

18.1

4.14

131.24

6.9472

30/04/2008

8.5

19.1

16.9

4.14

121.62

7.0002

31/03/2008

8.3

18.3

16.3

4.14

111.91

7.019

29/02/2008

8.7

19.2

17.5

4.14

104.99

7.1058

31/01/2008

7.1

20.7

18.9

4.14

96.89

7.1853

31/12/2007

6.5

21

16.7

3.33

104.26

7.3046

30/11/2007

6.9

21.7

18.5

3.33

98.53

7.3997

31/10/2007

6.5

22.2

18.5

3.33

92.66

7.4692

30/09/2007

6.2

22.1

18.5

3.33

86.3

7.5108

31/08/2007

6.5

22.8

18.1

3.33

77.9

7.5607

31/07/2007

5.6

20.9

18.5

3.33

79.2

7.5737

30/06/2007

4.4

20.9

17.1

3.33

75.28

7.6155

31/05/2007

3.4

19.3

16.7

3.33

75.83

7.6506

30/04/2007

3

20

17.1

3.33

75.09

7.7055

31/03/2007

3.3

19.8

17.3

3.33

73.73

7.7342

28/02/2007

2.7

21

17.8

3.33

66.48

7.7409

31/01/2007

2.2

20.21

15.93

3.33

62.73

7.7776

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