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RE: st: RE: Econometrics question


From   "Martin Weiss" <martin.weiss1@gmx.de>
To   <statalist@hsphsun2.harvard.edu>
Subject   RE: st: RE: Econometrics question
Date   Mon, 29 Mar 2010 21:06:38 +0200

<>

Even w/o other covariates, the mystery lingers on :-)


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


HTH
Martin


-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Christopher
Hajzler
Sent: Montag, 29. März 2010 21:02
To: statalist@hsphsun2.harvard.edu
Subject: Re: st: RE: Econometrics question

In terms of the actual value of the estimated coefficient,my sense is
that your intuition is correct in the case of a univariate regression.
 With more than one independent variable, we need to take into account
potential multicolinearity, and any potential impact changes in the
average of these variables across subperiods have on the estimates.

Try running the regressions again having dropped the other variables
and see what you get.

Cheers,
Chris


On Tue, Mar 30, 2010 at 7:50 AM, Martin Weiss <martin.weiss1@gmx.de> wrote:
>
> <>
>
> 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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