How bad is it to use pooled OLS instead of fixed effects when you have 7 years of panel data? From what I have understood, the risk is that the coefficients will be correlated with the error term, thus making the estimates biased. There will be some form of endogeneity.
Would it help if I include year dummies in the pooled OLS regression? It still wouldn’t capture the effects of varying intercept in the individual dimension, right?
One of my major explanatory variables is significant at the 5% level in FE regression. In the pooled OLS it is significant at the 0.001 level. Is this result negligible or could it still be used with the reservation that it is overestimated?
I ask this because most of the estimated parameters are strongly significant in the pooled OLS regression. Also, two of my explanatory variables that are constant get dropped in the FE regression. Although they are of secondary interest they contribute by explaining quite a lot of the variation in the dependent variable. (The sample is btw not congruent with a random effects model).
Is there some way to decide which model might be more suitable? If you know some things I should keep in mind when implementing the models I would be very grateful to hear them!
(I asked this question at another forum. I'll update if I get an answer. http://www.talkstats.com/showthread.php/56320-Using-pooled-OLS-when-running-a-model-with-panel-data?p=159061#post159061 )