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Monday, December 20, 2010
Fixed Effect Model: Panel Data Analysis
Fixed Effects Models
(very important stuff)
fixed effects models
rely on within-group action,
In the basic
, the effect of each predictor variable (i.e., the slope) is
This concept of “before and after” offers some insight into the estimation of
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
is that you cannot assess the
that have little within-group variation. For example, if you want to know the
of spectator sports
attendance on the demand for massages, you might not be able to use a
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
of a variable that does not show much within-group variation,
then you will have to forego
estimation. But this exposes you to potential omitted variable bias.
Unfortunately, there is no easy solution to this dilemma.
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
, 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,
regression will eliminate omitted variable bias.
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