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# AW: st: Coefficients resulting from Cross-Sectional Regressions

 From Fabian Schönenberger To Subject AW: st: Coefficients resulting from Cross-Sectional Regressions Date Fri, 6 Apr 2012 20:30:00 +0200

```I do not have the same number of yearly observations for each firm.
According to other studies from journals this is true for many empirical
investigations.

-----Ursprüngliche Nachricht-----
Von: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] Im Auftrag von Nick Cox
Gesendet: Freitag, 6. April 2012 19:49
An: statalist@hsphsun2.harvard.edu
Betreff: Re: st: Coefficients resulting from Cross-Sectional Regressions

What does "different amounts of yearly observations" mean?

Perhaps you seek -rolling-.

I'm not an economist, or at least no longer studying it, so economists and
econometricians on the list may have a better sense of what you want.

Nick

2012/4/6 Fabian Schönenberger <fabian.schoenenberger@gmail.com>:
> I try to explain the research idea. Based on the expression yid,t = a
> + bid,t*xid,t + e I am looking for bid,t, where id = firm and t = firm
years.
> Y = lnebit, x = lnsales; for every firm on different amounts of yearly
> observations. The coefficient b measures the sensitivity of ebit
> resulting from business activity. I will further analyse the
> coefficients and try to find out what financial policies (for instance
> equity-ratio) this dependence creates. In order to do that I (think I)
> need b for each individual for every point in time. Clustered b (on
> id) are not sufficient to reuse the coefficients for regressions based
> on yearly observations. One way would be to merge id with t, in order
> to get yearly coefficients. Doesn't this make sense?
>
> Fabian
>
>
> -----Ursprüngliche Nachricht-----
> Von: owner-statalist@hsphsun2.harvard.edu
> [mailto:owner-statalist@hsphsun2.harvard.edu] Im Auftrag von Nick Cox
> Gesendet: Freitag, 6. April 2012 18:39
> An: statalist@hsphsun2.harvard.edu
> Betreff: Re: st: Coefficients resulting from Cross-Sectional
> Regressions
>
> That sounds like a series of regressions based on precisely one data
> point each.
>
> And that makes no sense to me.
>
> Nick
>
> 2012/4/6 Fabian Schönenberger <fabian.schoenenberger@gmail.com>:
>> Then I am on the wrong track anyway. Which is the right formula to do
>> panel regressions with ID and t, for every single ID on each point in
>> time in order to get coefficients individualised by ID and t?
>>
>>
>> -----Ursprüngliche Nachricht-----
>> Von: owner-statalist@hsphsun2.harvard.edu
>> [mailto:owner-statalist@hsphsun2.harvard.edu] Im Auftrag von Nick Cox
>> Gesendet: Freitag, 6. April 2012 17:20
>> An: statalist@hsphsun2.harvard.edu
>> Betreff: Re: st: Coefficients resulting from Cross-Sectional
>> Regressions
>>
>> statsby _b, by(gvkey) saving(coefsales): regress lnebit lnsales
>>
>> conducts separate regressions for each firm. The coefficient does not
>> differ by year, as different years are pooled in each regression.
>>
>> You can put the results of -statsby- back in the original dataset by
>> using -merge-.
>>
>> Alternatively,
>>
>> egen group = group(gvkey)
>> gen coeff = .
>> su group, meanonly
>>
>> qui forval i = 1/`r(max)'  {
>>          regress lnebit lnsales if group == `i'
>>          replace coeff = _b[lnsales] if group == `i'
>> }
>>
>> Nick
>>
>> 2012/4/6 Fabian Schönenberger <fabian.schoenenberger@gmail.com>:
>>
>>> I am an absolute beginner in Stata and face now some challenges with
>>> generating regression coefficients as new variable for further
>> computations.
>>> I use tsset id t to conduct cross-sectional regressions. ID is gvkey
>>> which identifies firm (about 4?000 firms in my sample), t is fyear
>>> which identifies the specific year (ranging from 1984 to 2010). What
>>> I am looking for is the function to run cross-sectional regressions
>>> and generating coefficients as new variable to my list of variables.
>>> In other words: I am looking for b, specified for each ID for every
>>> t (yi,t =
>> ai + bi,t*xi,t).
>>> I have tried the following:
>>> statsby _b, by(gvkey) saving(coefsales): regress lnebit lnsales
>>> However, two problems arise. First, the major problem, I only get
>>> coefficients for gvkey, but not specified for every point in time
> (fyear).
>>> Second, the coefsales are safed in a new file, but I would prefer a
>>> new variable in my list of variable.
>>> Any suggestions to solve the two problems are highly appreciated.
>
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```