09 Jun 2015, 08:08
I'm not sure what you mean by "the multicollinearity test." If you are planning on doing some regressions and are concerned about multicollinearity among your variables, or multicollinearity between your variables and the panel identifier, just do that regression. If there is multicollinearity, Stata will omit one or more variables to eliminate it, and will tell you so in a message.

If you are concerned about near multicollinearity and want to see, for example, variance inflation factors, run -regress- using the variables you are interested in. After the regression, run -estat vif-.

That said, I think people worry about near multicollinearity too much. If you estimate your model and all the standard errors are reasonable, that is all you really want from a model, and even if the VIF's were really high, I would ignore them. If your model gives you unreasonably large standard errors for some variables that have a high VIF, there really isn't anything you can do about it any way unless there is some way to get your hands on a lot more data.

夏目贵志 发表于 2015-11-1 09:24
引用stata论坛里clyde的答案：