在Eviewss5.0中如何做Granger检验

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那位大侠详细的介绍一下步骤：

在Eviewss5.0中如何做Granger检验

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关键词：Granger EVIEWS Grange Anger range 检验 Granger

The Granger (1969) approach to the question of whether x causes y is to see how much of the current y can be explained by past values of y and then to see whether adding lagged values of x can improve the explanation. y is said to be Granger-caused by x if x helps in the prediction of , or equivalently if the coefficients on the lagged x’s are statistically significant. Note that two-way causation is frequently the case; x Granger causes y and Granger causes x.

It is important to note that the statement “x Granger causes y” does not imply that is the effect or the result of x. Granger causality measures precedence and information content but does not by itself indicate causality in the more common use of the term.

When you select the Granger Causality view, you will first see a dialog box asking for the number of lags to use in the test regressions. In general, it is better to use more rather than fewer lags, since the theory is couched in terms of the relevance of all past information. You should pick a lag length, , that corresponds to reasonable beliefs about the longest time over which one of the variables could help predict the other.

EViews runs bivariate regressions of the form:
y(t)=a(0)+a(1)y(t-1)+...+a(l)y(t-l)+b(1)x(t-1)+...+b(l)x(t-l)+e(t)

x(t)=a(0)+a(1)x(t-1)+...+a(l)x(t-l)+b(1)y(t-1)+...+b(l)y(t-l)+e(t)

for all possible pairs of (x, y) series in the group. The reported F-statistics are the Wald statistics for the joint hypothesis:

b(1)=b(2)=...=b(l)=0

for each equation. The null hypothesis is that x does not Granger-cause y in the first regression and that y does not Granger-cause x in the second regression.

Granger causality test:

Performs pairwise Granger causality tests between (all possible) pairs of the listed series or group of series.
Syntax
Command: cause(n, options) ser1 ser2 ser3
Group View: group_name.cause(n, options)

格兰杰因果检验要求变量为平稳的

首先对原数据进行ADF检验，如果数据平稳，可以直接作检验

如果原数据不平稳，而一阶差分平稳，那么就对数据的一阶差分进行检验

具体步骤：首先把数据打开，然后view-granger causality,然后输入滞后期，就可以了

补充一点： 滞后期的选择在格兰杰因果检验中非常重要，滞后期不同结果也不同，一般情况下，按照AIC和SC确定

可以在GROUP和VAR模型中做GRANGER检验，一般在GROUP中做较简单，在VAR模型中做较复杂，但在VAR中可得出短期和长期的GRANGER原因，在GROUP中做就是先打一个GROUP，再VIEW/GRANGER TEST 在对话框中选择滞后期，GRANGER检验对滞后期的选择很敏感，不同的滞后期会得出不同的结果。