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From: Peter Perkins <Peter.PerkinsRemoveThis@mathworks.com>
Newsgroups: comp.soft-sys.matlab
Subject: Re: Cross-Sectional Regression
Date: Wed, 25 Feb 2009 14:49:40 -0500
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Hans Schmidt wrote:
> Peter Perkins <Peter.PerkinsRemoveThis@mathworks.com> wrote in message <gnudvj$c1o$1@fred.mathworks.com>...
>> Hans Schmidt wrote:
>>
>>> Assume I have T=100 observations of returns. Furthermore, I have two risk factors (regressors) with each 100 observations. Now I use the first 60 observations to estimate the coefficients via a time series regression. This leaves me with the observations 61 to 100 for cross sectional tests. For this, suppose I have these 61 to 100 for, say, 10 firms. So my cross sectional test would be, whether the two estimated coefficients explain each (61, 62, ..., 100) cross section of stock returns. So I should get 2 (risk factors) * 10 (number of firms) parameter estimates (when excluding a constant) for each cross section.
>> Hans, I'm not an econometrician.  As I read your description, you want to do 40 linear regressions, each with 10 observations and each involving 20 or perhaps 21 coefficients.  Obviously that makes no sense.  Perhaps you mean that you want to estimate the _same_ 21 coefficients across all 40 times.  That s easy to do.  Perhaps you mean something else.
> 
> Hey Peter,
> 
> I try to regress my 2 coefficients on each observation for each firm. So for instance, I start with t=61 and regress my 2 coefficients on each firm's observation separately. So I should get 2 (or 3 with a constant) estimates for firm 1, 2 (or 3) estimates for firm 2, etc. With ten firms, I get 2 (or 3) times 10 estimated parameters in the first cross section.
> 
> This procedure is repeated for t=62, t=63,...,100.

Hans, again, as I read your description, you are asking to estimate 20 (or 30) parameters using only 10 observations.  You may well be using standard finance terminology, but I'm not following.  REGRESS can fit any linear rgression you want, but I can't tell you how to set up the design matrix unless you describe the model that you want to fit very specifically in terms of what data you are using to estimate which parameters.