Re: st: how can i make my loop run faster?

[Partho Sarkar <partho.ss+lists@gmail.com>]

I think the  -rolling- time series command can help do this.  E.g.
once you a) tsset the panel as before, and b) sort the dataset by
-sort panelvar datevar-

rolling _b,w(20)  saving(tryroll): regress y x

would divide up your entire time span into overlapping windows of
width 20, run a regression for each panel in each window, and save the
panel ids, the start & end of each window, and the regression
coefficients, in a Stata data file called "tryroll".

See -help rolling- and the manual entry for details & examples.  Given
your special requirements, you will probably have to do this in 2 or
more steps, and manipulate the results further to get exactly what you
want.

Partho

On Mon, Sep 19, 2011 at 8:20 PM, Stefano Rossi <sr525@cornell.edu> wrote:
> Partho,
>
> Many thanks for this, it is very helpful.
>
> This raises one question, though: a crucial part of my procedure is that I need to run regressions only on 12 observations for each firm-period pair; that is, if a firm i has data back to period t=-50, say, I still have to run the regression only on the 12 observations from -1 to -12, ignoring all others.  This worked well with my loop, but I do not see readily how to do this with statsby.  Can you please advise?
>
> Best,
>
> Stefano
>
>
>
> -----Original Message-----
> From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Partho Sarkar
> Sent: Monday, September 19, 2011 1:06 AM
> To: statalist@hsphsun2.harvard.edu
> Subject: Re: st: how can i make my loop run faster?
>
> Stefano
>
> You don't seem to be actually making any use of the panel structure of
> the data.  Stata has very neat built-in procedures for dealing with
> such data.
>
> Very briefly, 2 pointers (I am ignoring the special wrinkle in your
> problem that you want to run 20 seoarate regressions for each "firm
> i-period t" pair- you would have to adapt the procedure accordingly):
>
> A.  I would use -tsfill, full- to fill in the time values and balance the panel.
>
> B. If you use tsset panelvar datavar (or xtset), where panelvar is
> your panel identifier, and datevar the date variable, you can use:
>
> statsby _b _se, by(panelvar): regress y x
>
> to do all the regressions in one go (assuming a single regression for
> each "firm i-period t" pair), rather than separately within a long
> loop.   You can collect the results saved in r-class macros, as with
> _b & _se above.  See -help statsby-
>
> Having said all that, I have never tried to run a set of regressions
> with 30,000 firms & 200 time periods in a single run of a program!!!
> I suspect this will be painfully slow no matter how efficient your
> code. An obvious alternative would be to split the firms into, say, 10
> subsets, do the regression for each subset, and put all the results
> together.
>
> Hope this helps
>
> Partho Sarkar
> Consultant Econometrician
> Indicus Analytics
> New Delhi, India
>
>
> On Mon, Sep 19, 2011 at 5:22 AM, Stefano Rossi <sr525@cornell.edu> wrote:
>> Dear Statalist Users,
>>
>> I wonder if you can help me make a faster loop?
>> I have an unbalanced panel of about 30,000 firms and 200 periods, and for each "firm i-period t" pair I need to run 10 regressions on the 12 observations from t-1 to t-12 of the same firm i, and another 10 regressions on the observations from t+1 to t+12 of the same firm i.  I have come up with the following program, which works well as it does what it should do, but it is very slow (due to the many ifs I suspect) - here's a simplified version of it with just two regressions:
>>
>> forval z = 1/30000 {
>> levelsof period if firm==`z', local(sample)
>> foreach j of local sample {
>>       local k = `j' - 13
>>       capture reg y x if firm ==`z' & period<`j' & period>`k' & indicator==1
>>       if _rc==0 {
>>       predict y_hat, xb
>>       replace before = y_hat[_n-1] if firm == `z' & period == `j'
>>       drop y_hat
>>       }
>>       local w = `j' + 13
>>       capture reg y x if firm ==`z' & period>`j' & period<`w' & indicator==1
>>       if _rc==0 {
>>       predict y_hat, xb
>> replace after = y_hat[_n+1] if firm == `z' & period == `j'
>>       drop y_hat
>>       }
>>       }
>> }
>>
>> Right now, it takes several minutes for each firm, so if I run it for the whole sample it would take weeks.
>> Is there any way to make it (a lot) faster?
>>
>>
>> *
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>>
>
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