Monday, December 20, 2010

Fixed Effect Model: Panel Data Analysis

Because fixed effects models rely on within-group action, ...
In the basic fixed effects model, the effect of each predictor variable (i.e., the slope) is...
This concept of “before and after” offers some insight into the estimation of fixed effects models.
Because fixed effects models rely on within-group action, you need repeated observations for each group, 
and a reasonable amount of variation of your key X variables within each group.
One potentially significant limitation of fixed effects models is that you cannot assess the effect of variables 
that have little within-group variation. For example, if you want to know the effect of spectator sports 
attendance on the demand for massages, you might not be able to use a fixedeffects model
because sports attendance within a city does not vary very much from one year to the next. 
If it is crucial that you learn the effectof a variable that does not show much within-group variation, 
then you will have to forego fixed effects estimation. But this exposes you to potential omitted variable bias. 
Unfortunately, there is no easy solution to this dilemma.
Fixed effects regressions are very important because data often fall into categories such as 
industries, states, families, etc. When you have data that fall into such categories, 
you will normally want to control for characteristics of those categories that might affect the LHS variable. 
Unfortunately, you can never be certain that you have all the relevant control variables, 
so if you estimate a plain vanilla OLS model, you will have to worry about unobservable factors 
that are correlated with the variables that you included in the regression. 
Omitted variable bias would result. If you believe that these unobservable factors are time-invariant, 
then fixed effects regression will eliminate omitted variable bias.

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