Granger causality analysis based on chinas inflation analysis - Free Essay Example

Sample details Pages: 3 Words: 1019 Downloads: 3 Date added: 2017/06/26 Category Finance Essay Type Analytical essay Tags: China Essay Inflation Essay Did you like this example? Financial sector in the formation of the reasons for inflation has been, and there is no consensus. Keynesian view that the real economy is highly unstable, the labor market is the root of inflation, and thus derive the famous Phillips curve, monetary school believes that inflation is essentially a monetary phenomenon, while the market is essentially stable, no need for government to artificially adjust. The use of macroeconomic policies, inflation, structure, school tried to economic productivity of various departments to explain the long-term trends are inconsistent prices and the resulting inflation. Don’t waste time! Our writers will create an original "Granger causality analysis based on chinas inflation analysis" essay for you Create order These three schools of thought in theory, to some extent explains the causes of inflation, but can not fully explain the formation mechanism of inflation, and given the corresponding policy recommendations. Inflation is a comprehensive economic phenomenon, rather than single-factor effects. In this paper, econometric methods, the use of ADF model (Augmented Dickey-Fuller test) and the Granger causality test (Granger Causality Tests) model, from the GOP Growth, M1 Growth, Consume Growth, Wage Growth, Exchange Rate these economic indicators Inflation Rate in effect to analyze the causes of inflation. 2. Analysis In the time-series case, the two economic variables X, Y Granger causality between the defined as: if the variable contains the X, Y under the conditions of the past information on the predictive variable Y is superior to solely focus from the Ys past information to forecast Y effect that the variable X helps to explain the future change in the variable Y is considered a variable X is caused by the variable Y Granger causes. Granger causality test is essentially testing whether a variable lag variables other variables could be introduced into the equation. If a variable is subject to the delayed impact of other variables, claimed that they have Granger causality.   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Right and two time series, based on Grangers definition, if a relative value only to predict the past when the past values can be used to improve forecasts. That is, if the past value can statistically improve the forecast is said to cause and effect in the. Granger causality test can not only long-term relationship between the variables tested, but also on the short-term relationship between the variables tested. There is no unit root for the two stationary series, can be defined the following equation:  ¼Ãƒâ€¹Ã¢â‚¬  1 ¼Ãƒ ¢Ã¢â€š ¬Ã‚ ° Test from the causal relationship, namely, the zero hypothesis testing. Original hypothesis; alternative hypothesis. If you accept the original hypothesis, then there is no causal relationship from to; On the contrary, then there exists a causal relationship from to. Granger causality test for a pre-condition that the time series must be stationary, or may be a false returns. Therefore, before carrying out Granger causality test first response to the various indicators of stability of time series to conduct a unit root test (unit root test). ADF tests were used to smooth sequences of various indicators to conduct a unit root test. 3. Empirical Analysis Paper selected 1980-2007 Year Inflation Rate (IR), G OP Growth (GOP), M1 Growth (M1), Consume Growth (CONSUME), Wage Growth (W), Exchange Rate (EX) 6 variables, 28 samples the number of the study. Eviews6.0 calculated using the software. Inflation Rate (IR), GOP Growth (GOP), M1 Growth (M1), Consume Growth (CONSUME), Wage Growth (W), Exchange Rate (EX) 6 variables of the line graph the figure below. Figure 1 3.1 Unit root test In time series analysis, to avoid spurious regression phenomenon, in doing causality analysis, the need for variable ADF test (Augmented Dickey-Fuller test). Test results shown in Table 1: TABLE 1 Unit root test Variable Augmented Dickey-Fuller test statistic Test critical values 1% level 5% level 10% level IR -3.180069 -3.711457 -2.981038 -2.629906 I(0) GOP -3.254578 -3.737853 -2.991878 -2.635542 I(0) M1 -4.337122 -3.711457 -2.981038 -2.629906 I(0) CONSUME -5.318454 -3.711457 -2.981038 -2.629906 I(0) W 0.651459 -3.769597 -3.004861 -2.642242 I(1) D(W) -5.975410 -3.769597 -3.004861 -2.642242 I(0) EX -1.661010 -3.699871 -2.976263 -2.627420 I(1) D(EX) -4.669889 -3.711457 -2.981038 -2.629906 I(0) IR (Inflation Rate), GOP Growth, M1 Growth, C Growth (Consume Growth), the amount of the ADF test at the 5% significance level less than the corresponding critical value, there is no unit root, is smooth. The W Growth (Wage Growth), EX (Exchange Rate) the amount of ADF test at the 5% significance level is greater than the corresponding critical value, there are unit root is not smooth, but the first-order differential sequence of ADF values are less than 5% significance level the critical value, indicating they do not exist unit root, is a first-order stationary 3.2Granger Causality Tests As the W Growth (Wage Growth), EX (Exchange Rate) is a stable order, so DW respectively, said W Growth (Wage Growth) first-order differential, DEX that EX (Exchange Rate) of the first-order differential, and separately GOP, M1, CONSUME, DW, DEX and IR for Granger Causality Tests, test results shown in Table 2 TABLE 2 Granger Causality Tests Null Hypothesis Obs F-Statistic Prob. GOP does not Granger Cause IR 25 3.61448 0.0334 IR does not Granger Cause GOP 1.99679 0.1506 M1 does not Granger Cause IR 25 13.2070 8.E-05 IR does not Granger Cause M1 0.21912 0.8818 CONSUME does not Granger Cause IR 25 0.40692 0.7499 IR does not Granger Cause CONSUME 0.74334 0.5401 DW does not Granger Cause IR 24 2.36992 0.1025 IR does not Granger Cause DW 5.02768 0.0101 DEX does not Granger Cause IR 24 0.95259 0.4375 IR does not Granger Cause DEX 0.81532 0.5030 According to AIC and th e SC minimum principle to determine the optimal lag order is 3, the corresponding results can be seen GOP and the M1 are the reasons for IR, but CONSUME, W, and EX are not the reasons for IR, indicating GOP and the M1 have an impact on inflation. 4. Summary This paper analyzes the Inflation Rate (IR), GOP Growth (GOP), M1 Growth (M1), Consume Growth (CONSUME), Wage Growth (W), Exchange Rate (EX) the relationship between the use of ADF test and Granger causality test empirical analysis of various factors, analysis found that: First, GOP Growth, M1 Growth ahead of inflation and changes in the movement, so GOP Growth, M1 Growth Inflation in China has a sense of Granger causality; but Consume Growth, Wage Growth, Exchange Rate and inflation causal no significant relationship. Secondly, M1 Growth and Inflation Rate changes in the same direction exists between the relationship and M1 Growth impact on inflation there is a certain lag, which shows inflation is essentially a monetary phenomenon.