Granger causality analysis based on chinas inflation analysis

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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. 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 Y’s 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.    Right and two time series, based on Granger’s 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: ¼ˆ1¼‰ 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;

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