Fixed Effects Model - Testing FE Vs. RE

Testing FE Vs. RE

We can test whether a fixed or random effects model is appropriate using a Hausman test.

:
:

If is true, both and are consistent, but only is efficient. If is true, is consistent and is not.

\widehat{HT}=T\widehat{Q}^{\prime}[Var(\widehat{\beta}_{FE})-Var(\widehat
{\beta}_{RE})]\widehat{Q}\sim\chi_{K}^{2} where

The Hausman test is a specification test so a large test statistic might be indication that there might be Errors in Variables (EIV) or our model is misspecified. If the FE assumption is true, we should find that \widehat
{\beta}_{LD}\approx\widehat{\beta}_{FD}\approx\widehat{\beta}_{FE}.

A simple heuristic is that if \left\vert \widehat{\beta}_{LD}\right\vert
>\left\vert \widehat{\beta}_{FE}\right\vert >\left\vert \widehat{\beta}
_{FD}\right\vert there could be EIV.

Read more about this topic:  Fixed Effects Model

Famous quotes containing the word testing:

    Today so much rebellion is aimless and demoralizing precisely because children have no values to challenge. Teenage rebellion is a testing process in which young people try out various values in order to make them their own. But during those years of trial, error, embarrassment, a child needs family standards to fall back on, reliable habits of thought and feeling that provide security and protection.
    Neil Kurshan (20th century)