One way of thinking about fixed effects (in a linear model) is that a
dummy is added for each unit (minus one reference unit). These dummies
absorb all the observed and unobserved differences between the units,
so it does take the across variation into account. However, you can no
longer describe what a unit level variable, like the average value of
X, does to Y. In a random effects model you can describe the effects of
unit level variables, but at a price: you now have to make a number of
assumptions you did not have to make with a fixed effects model. The
assumption that people like least is the assumption that the random
effect is uncorrelated with the observed variables.
-- Maarten
--- yjh jsh <[email protected]> wrote:
> Dear all,
> I have a newbie question here. sorry for this.
>
> I have a panel data with variable Y and X for two units for example.
> unit year X Y
> 1 1991 1 20
> 1 1992 2 19
> 1 1993 3 21
> 2 1991 10 40
> 2 1992 11 40
> 2 1993 11 39
>
> That is, there is a larger cross variation than within variation
>
> As i understand, FE only address the variation within units. So, if I
> use FE, i will not find a significant relationship between x and y
> based on the nature of the hypothectical data.
> but this finding does not take into account the fact Y takes higher
> vaue in unit 2 because x takes higher value in that unit. That is, fe
> failed to represent the across-variation.
>
> Is my understanding correct?
>
> Sorry for this simple question.
>
> best
> *
> * For searches and help try:
> * http://www.stata.com/help.cgi?search
> * http://www.stata.com/support/statalist/faq
> * http://www.ats.ucla.edu/stat/stata/
>
-----------------------------------------
Maarten L. Buis
Department of Social Research Methodology
Vrije Universiteit Amsterdam
Boelelaan 1081
1081 HV Amsterdam
The Netherlands
visiting address:
Buitenveldertselaan 3 (Metropolitan), room Z434
+31 20 5986715
http://home.fsw.vu.nl/m.buis/
-----------------------------------------
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*
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