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I am trying to analyze a panel data which includes observations for each US state collected across 45 years. I have two predictor variables that vary across time (A,B) and one that does not vary (C). I am especially interested in knowing the effect of C on the dependent variable Y, while controlling for A and B, and for the differences across states and time.

This is the model that I have, using plm package in R.

random <- plm(Y~log1p(A)+B+C, index=c("state","year"),model="random",data=data)

My reasoning is that with a time invariant variable I should be using random rather than fixed effect model. My question is: Is my model and thinking correct?

Thank you for your help in advance.

landroni
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user3524077
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1 Answers1

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You base your answer about the decision between fixed and random effect soley on computational grounds. Please see the specific assumptions associated with the different models. The Hausman test is often used to discriminate between the fixed and the random effects model, but should not be taken as the definite answer (any good textbook will have further details).

Also pooled OLS could yield a good model, if it applies. Computationally, pooled OLS will also give you estimates for time-invariant variables.

Helix123
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