Panel data: fixed individual and random time effect I am currently working with panel data. So far, I specified a fixed effects model with two fixed effects: firm fixed effects (I'm working with company data) and time fixed effects. After performing some Hausman tests, the results suggest that the firm effect is indeed fixed, but my time effect seems to be random.
To get to this conclusion, I tested my two-way fixed effects model against (1) the two-way random effects model, (2) a model with fixed time and random firm effects, and (3) a model with fixed firm and random time effects. All but the third model suggested the use of the two-way fixed effects model. So I concluded that I should use the model with the fixed firm and random time effects.
Now my questions: 


*

*Is it even possible to have one random and one fixed effect?

*Do my tests make sense in this way?

*How would you estimate such a model? I simply added a full set of firm/time dummies to my regression and then used the random effects estimators, but I really don't know if this makes sense.

*Can you recommend any literature in which a model with one fixed and one random effect is explained?
Thank you very much!
 A: *

*Yes, this is a "mixed effects" model.

*I am not a fan of diagnostic testing as a way of "discovering the question" your model is answering. A caveat to consider is the non-transitivity of tests. When you go about testing 3 (or more) models, you may end up with an ourobos where model A > model B, model B > model C, yet model C > model A. It makes no sense. The choice of whether to apply fixed or random effects is based on the scientific rationale. So I say your tests do not make sense.

*Generally, if there is power to fit a fixed effects model, we prefer that approach because the model enables direct inference and prediction from estimated effects with fewer asymptotic results. A GEE blends many of these properties: easy inference, prediction, but accounting for correlated observations. I would fit a GEE knowing little else about your problem.

*Virtually any data analysis text on the subject of longitudinalk data discusses this. "Longitudinal data analysis" by Diggle, Heagerty, Liang, Zeger is one such text I frequently access.
