stata自相关和多重共线性问题

关键词：自相关和多重共线性、共线性与自相关区别、多重共线性 自相关、stata 多重共线性检验、stata自相关检验

1). 自相关

Autocorrelation

Iterated GLS with autocorrelation does not produce the maximum likehood estimates, so we cannot use the likelihood-ratio test procedure, as with heteroskedasticity. However, Wooldridge (2002, 282–283) derives a simple test for autocorrelation in panel-data models. Drukker (2003) provides simulation results showing that the test has good size and power properties in reasonably sized samples.

There is a user-written program, called xtserial, written by David Drukker to perform this test in Stata. To install this user-written program, type

. findit xtserial . net sj 3-2 st0039 (or click on st0039) . net install st0039 (or click on click here to install)

To use xtserial, you simply specify the dependent and independent variables:

. xtserial depvar indepvars

A significant test statistic indicates the presence of serial correlation.

2). 多重共线性

Multicolinearity

Stata provides a built-in measure of multicolinearity, the variance inflation factor (VIF). To use the VIF, first estimate the regression equation, then type the command vif. below is an example:

reg y x5-x7
vif

As a rule of thumb, a variable whose VIF values are greater than 10 may merit further investigation. Tolerance, defined as 1/VIF, is used by many researchers to check on the degree of collinearity. A tolerance value lower than 0.1 is comparable to a VIF of 10. It means that the variable could be considered as a linear combination of other independent variables.

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