# interpretation of Hausman test results

I used Hausman test in R in order to decide whether I should use fixed effects or random effects model. This is the result I got:

Hausman Test

data: Deviation ~ Concentration chisq = 1.721, df = 1, p-value = 0.1896 alternative hypothesis: one model is inconsistent

I would appreciate some help in interpreting this result (I have not studied Statistics ever, and I am yet facing this challenge), and which model should I use - fe or re?

thank you!!

• – whuber
Aug 16, 2017 at 16:50

When the $p$-value is low, commonly less than $0.05$, the $H_0$ must go!

In short the Hausman test (sometimes also called Durbin--Wu--Hausman test) in R assumes $H_{0}$ is that the preferred model is random effects, i.e. no significant correlation vs. the alternative, $H_{a}$, the fixed effects, i.e. whether the errors ($\mu_i$) are correlated with the regressors, see see Section 4.3 in Baltagi (2005).

Running ?plm::phtest in R would give you further details and refrences.

--

Baltagi (2005), Econometric Analysis Of Panel Data

• Catchy phrase, but not necessarily exhaustive. But granted, without at least some statistics background it will be hard to give a more refined answer. But maybe the original question is not suited for a non-statistical answer. May 25, 2018 at 17:41
• Good points. The answer linked in the comments does provide an exhaustive answer. I mainly wanted to provide something accessible in line with the answer.
– uT5r
May 26, 2018 at 17:30