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st: RE: Econometrics question


From   "Martin Weiss" <martin.weiss1@gmx.de>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: RE: Econometrics question
Date   Mon, 29 Mar 2010 20:50:50 +0200

<>

The significance also depends on the number of observations in the
estimation sample, so the "weighted average" need not carry over to the
standard error estimation:

***
sysuse auto, clear
reg price weight length
est store full
reg price weight length in 1/`=_N/2'
est store firsthalf
reg price weight length in `=_N/2+1'/l
est store secondhalf
estimates table full firsthalf secondhalf, se style(oneline)
***


HTH
Martin


-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of kokootchke
Sent: Montag, 29. März 2010 20:39
To: statalist
Subject: st: Econometrics question

Dear Stata users, 

I have a basic econometric question and I'm hoping you can help me out. I am
running a regression of bond spreads on various variables denoting domestic
economic conditions, and country fixed effects; I'm clustering my standard
errors by quarter, e.g.

xi: regress LogSpread GDPgrowth DebtToGDP i.country, cluster(time)

I have quarterly data for 40 different countries, although it's a very
unbalanced panel because the spread of the bond is for new bond issues and a
lot of countries don't issue new bonds every quarter. So, the data would
look something like this:

Country   Time   Spread GDPgrowth 
Argentina 1991q1 400    3.0
Argentina 1994q4 450    2.5
Argentina 2001q3 800    0.7
Brazil    1993q2 ...
Brazil    1993q4 ...
Brazil    1994q1 ...
Colombia ...
...

When I run a simple regression like the one above for the full sample, I
obtain a coefficient for GDPgrowth of -0.073***

Then if I run this same regression for two separate subsamples for the years
1991-1997 and 1998-2006, my coefficients for GDPgrowth are -0.056 and 0.009,
both insignificant.

In my experience, the full sample coefficient would in general be some sort
of weighted average of the two coefficients obtained from subsample
regressions. So, I don't understand why this is not the case here... 

The number of observations in the two subsamples add up to the number of
observations in the full sample estimations.

Any ideas?

Thanks!
Adrian




 		 	   		  
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