Kit Baum wrote:
> Apples and oranges make a good fruit salad, but...
>
> The areg shows 11 region categories. The xtreg shows 304 groups of
> 'pano'. If you use the same variable (e.g. region) in both commands,
> you will get the same coefficients/std errors, or your computer is
> broken.
As a gangster from the East End of London might say, I've sorted it. I
used my PANO (constituency) variable as the fixed effect in both models,
after Scott Merryman's corrective suggestion. This got me the identical
results I should have had for both LSDV and FE. I could have used REGION
as my fixed effect, but the codes in this variable represent much larger
areas of the UK. I prefer the former, since one cannot get much more
precise regional FEs than parliamentary constituencies (unless you use
local government wards, but that would be masochistic)!
However, I noticed something in my two 'identical' models:
. areg edconch edround2 edround3 edtime edtimesq edyear edpollch lagconch
laglabch lagldmch clmargin ldmargin conplace edenp class if edmarker==1,
absorb(pano)
Number of obs = 1632
F( 14, 1314) = 69.42
Prob > F = 0.0000
R-squared = 0.7324
Adj R-squared = 0.6678
Root MSE = 6.1165
[...]
. tsset pano edyear
. xtreg edconch edround2 edround3 edtime edtimesq edyear edpollch lagconch
laglabch lagldmch clmargin ldmargin conplace edenp class if edmarker==1,
fe
Fixed-effects (within) regression Number of obs = 1632
Group variable (i): pano Number of groups = 304
R-sq: within = 0.4252 Obs per group: min = 1
between = 0.4157 avg = 5.4
overall = 0.3644 max = 10
F(14,1314) = 69.42
corr(u_i, Xb) = -0.5123 Prob > F = 0.0000
[...]
Now, I have fit the same models here (with the same FE in both) whilst
'switching off' any weighting and cluster options in both of them. How is
it that the R^2 is not the same in both models? Doubtless I'm overlooking
something. Ta.
CLIVE NICHOLAS |t: 0(044)191 222 5969
Politics |e: [email protected]
Newcastle University |http://www.ncl.ac.uk/geps
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