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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!!

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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

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  • $\begingroup$ 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. $\endgroup$ – cherub May 25 '18 at 17:41
  • $\begingroup$ 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. $\endgroup$ – uT5r May 26 '18 at 17:30

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