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Re: st: multiple rolling regressions


From   "Erasmo Giambona" <e.giambona@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: multiple rolling regressions
Date   Thu, 8 May 2008 19:21:53 +0200

Hi Austin,

Thanks very much. Your code really gets me very very close to what I
want to achieve.

Here are some additional clarifications that you required. Yes, I am
only interested in saving the estimated coefs d of reg ye xe. If there
are missing data in the 5 year window, I would still like to get the
estimates with whatever data is available. If there are gaps, for
instance for a company I have data for the window 1980-1984 and
1990-1994, then I would like to get estimates for the first window and
independently for the second window.

I noticed that you code calculates d based on observations from -2 to +2.

This is what I get if I run it:

  +-----------------------------------+
  | firm   year    y    x           d |
  |-----------------------------------|
  |    1   1980   20   23           . |
  |    1   1981   22   34           . |
  |    1   1982   20   19   .15129763 |
  |    1   1983   20   23    .1403674 |
  |    1   1984   22   34   .24162679 |
  |    1   1985   20   19   .15236427 |
  |    1   1986   28   34           . |
  |    1   1987   30   19           . |
  |-----------------------------------|

For instance, d=.15129763 for 1982 is based on data from 1980-1984.
What I would really like to achieve is for instance that d for 1984 is
based on the data from 1980-1984.

Hope you can provide some additional hints on how I could obtain what I need.
Thanks very much,
Erasmo


On Thu, May 8, 2008 at 4:21 PM, Austin Nichols <austinnichols@gmail.com> wrote:
> Erasmo Giambona <e.giambona@gmail.com>:
> You haven't said what output you hope to save from this exercise, so
> it's impossible to propose an actual solution--did you want to save
> the estimated coefs d?  Residuals?
>
> As written, if you go through the whole exercise, the dataset is
> unchanged at the end!
>
> Also, why not make the "trend" variable range -2 to 2 for 5 years, so
> the intercept is the mean of the depvar?  The residuals are the same
> either way, but you can derive and interpret the coefs in the latter
> case much more easily.
>
> If you start with some actual fake data (see below) and show what you
> hope to end up with, someone is bound to show you how to do it!  If
> you just want the estimated coefs d_{it}, and there are no gaps in
> year, here's one way to do it:
>
> clear
> input firm year y x
> 1 1980 20 23
> 1 1981 22 34
> 1 1982 20 19
> 1 1983 20 23
> 1 1984 22 34
> 1 1985 20 19
> 1 1986 28 34
> 1 1987 30 19
> 2 1980 15 23
> 2 1981 55 34
> 2 1982 29 19
> 2 1983 15 23
> 2 1984 55 34
> 2 1985 29 19
> 2 1986 28 34
> 2 1987 30 19
> end
> tsset firm year
> sort firm year
> g double d=.
> qui forv i=3/`=_N-2' {
>  loc f=`i'-2
>  loc l=`i'+2
>  reg y year in `f'/`l' if firm==firm[`i']
>  if e(N)==5 {
>  predict double ye if e(sample), resid
>  reg x year in `f'/`l' if firm==firm[`i']
>  predict double xe if e(sample), resid
>  reg ye xe
>  replace d=_b[xe] in `i'
>  drop ye xe
>  }
>  }
> li, noo sepby(firm)
>
> If you have gaps in year (which -tsset- will report above), or missing
> data, you will need to specify what you want to do in those cases...
>
>
> On Thu, May 8, 2008 at 5:50 AM, Erasmo Giambona <e.giambona@gmail.com> wrote:
> > Here is my problem:
> >
> > I have a panel dataset that looks as follows:
> >
> > firm year y x
> > 1 1980 20 23
> > 1 1981 22 34
> > 1 1982 20 19
> > 1 198320 23
> > 1 1984 22 34
> > 1 1985 20 19
> > 1 1986 28 34
> > 1 1987 30 19
> > ...........
> > 2 1980 15 23
> > 2 1981 55 34
> > 2 1982 29 19
> > 2 1983 15 23
> > 2 1984  55 34
> > 2 1985 29 19
> > 2 1986 28 34
> > 2 1987 30 19
> > .............
> > etc.
> >
> > Assume also that "trend" is a trend variables ranging from 1 to 5.
> >
> > I need to implement a two-step procedure. In the first step, I run for
> > each "firm" a regression of y on the trend variable and an intercept
> > and a regression of x on the trend variable and an intercept, using in
> > both cases 5 years of data: 1) y = intercept +b*trend + errory;
> > 2) x = intercept + c*trend + errorx. After the first step, I output
> > the errors from regression 1) and regression 2). Now I regress the
> > errors
> > from 1) on the errors from 2) (again for each firm): 3) errory =
> > d*errorx+error. I repeat the two steps for each year in the sample.
> >
> > I hope you have some suggestions on how I could handle this issue.
> >
> > Best regards,
> > Erasmo
>
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