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Re: st: correcting standard errors for model with repeated observations in same time period

From   "Austin Nichols" <>
Subject   Re: st: correcting standard errors for model with repeated observations in same time period
Date   Wed, 7 May 2008 08:11:37 -0400

Adrian de la Garza <>:
I can certainly see an argument for clustering on country (assuming
you have 50 or more in your data) to account for serial correlation
within country, or clustering on time (
 g time=yq(year,quarter)
 format time %tq
) to account for the correlation of errors across countries at a point
in time, but I can't see why you need to account for missing obs when
no bonds were issued.

Maybe you want two-way clustering along the lines of,

On Wed, May 7, 2008 at 1:14 AM, Adrian de la Garza
<> wrote:
> Dear all,
>  I am running a linear regression that looks like this:
>  bondspread = a + macro*b + covariates*c + public/private_dummies + country_FE + e
>  where bondspread is the spread of a bond issued by a public or a private entity in a given country in period (year, quarter); macro are a bunch of macroeconomic variables such as the growth of GDP, total external debt/GDP ratio, etc.; covariates are a bunch of individual characteristics; and I have a dummy that tells me whether the institution issuing the bond was public or private, and country fixed effects (and maybe I will add year FE later and other things... but this is what I have now).
>  The problem is that I think that I should correct the standard errors of a simple OLS because one same country may issue multiple bonds in a given (year,quarter)... so, what I am doing is to cluster by country or at least by region (like "Latin America" or "East Asia" and such)... but I think I should do something else. Does anybody have an idea of how to make such correction or whether it is necessary at all?
>  ...So, basically, Mexico can issue one or multiple bonds in a given period (because one of multiple different companies and local governments decide to issue debt) or can simply NOT issue at all (when spread is missing).
>  I know I probably need some sort of selection model (a la Heckman) to account for the fact that some countries may or may not issue at any given time... but do I need to account for the clumping of issues at any given time? And if so, how?
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