Simen gaure
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Thanks for contributing an answer to Cross Validated! In practice, the coefficient of the regressor of interest, x, is the same in both cases. When you apply lm to the demeaned data, lm does not know that the means have been subtracted, or equivalently, that you have eliminated the dummies for the cluster levels. However, its standard error and actually all the other relevant quantities of the regression: R squared, F test, etc. . One of the most direct ways is probably to use the -plm- package. The degrees of freedom is used to compute the standard errors, thus they are wrong for your demeaned lm.

It is quite convenient from a computational standpoint, because we are still controlling unobserved heterogeneity at clustering level, but we do not need to estimate all the time-fixed intercepts. I am fitting a Fixed-Effects model, with intercepts at cluster level. The R script is: set seed, load packages, create fake sample set. To learn more, see our. Provide details and share your research! Use MathJax to format equations. I have tried both of these approaches, and I came to a strange result.

If you look carefully at the output, you'll notice that the degrees of freedom are different. This second approach is usually referred to as the within transformation. . . .

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