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?
To be more specific, this is kind of like what I have:
country year quarter bondspread
Mexico 1990 1 200
Mexico 1990 2 .
Mexico 1990 3 .
Mexico 1990 4 170
Mexico 1990 4 224
Mexico 1990 4 272
Mexico 1991 1 .
Mexico 1991 2 168
...
Brazil 1990 1 .
Brazil 1990 2 315
Brazil 1990 2 295
Brazil 1990 3 .
Brazil 1990 4 .
Brazil 1991 1 292
Brazil 1991 1 350
Brazil 1991 1 264
...
and so on. 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?
Thank you very much!
Best,
Adrian
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