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RE: RE: st: ivreg2: interacting the endogenous regressor


From   <V.Monastiriotis@lse.ac.uk>
To   <statalist@hsphsun2.harvard.edu>
Subject   RE: RE: st: ivreg2: interacting the endogenous regressor
Date   Fri, 25 May 2012 20:00:26 +0100

Kit, many thanks for this - point taken :)

I was concerned that I somehow needed to have a predicted "en" which,
when multiplied by "ex", would be perfectly correlated with the
prediction of "ex_en". But I see why this is wrong: in Wooldridge's
terms, what I should be after is a "projection of the interaction"
(which is what you proposed) and not the "interaction of the projection"
(which is what I was after). 

===============================================
Dr Vassilis Monastiriotis
Hellenic Observatory, European Institute, LSE 
email: v.monastiriotis@lse.ac.uk 
===============================================

-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Christopher
Baum
Sent: 25 May 2012 18:40
To: statalist@hsphsun2.harvard.edu
Subject: Re: RE: st: ivreg2: interacting the endogenous regressor

On May 25, 2012, at 8:33 AM, statalist-digest wrote:

> Kit, from what I understand, your suggestion is to treat the two 
> variables (endogenous and interaction) as independent, thus running 
> something like:
> 		ivreg2 y ex (en en_ex = z z_ex)
> where 'ex' is the exogenous variable, 'en' is the endogenous variable,

> 'z' is the instrument (excluded variable), 'en_ex' is the interaction 
> between the endogenous and the exogenous variables and 'z_ex' is the 
> interaction between the exogenous variable and the instrument.
> 
> My question is: doesn't this assume that 'en' and 'en_ex' are 
> independent? In other words, isn't it that the first-stage regressions

> predict 'en' independently of the prediction of 'en_ex' (and, 
> inversely, predict 'en_ex' independently of the prediction of 'en')? 
> This would be more of a problem (I think) the higher the variance of 
> 'en' relative to that of 'ex'. But, importantly, if it is the other 
> way round (i.e., if the endogenous variable has a much lower variance 
> than the exogenous one, so that 'en' and 'en_ex' are not too 
> collinear), isn't it that my prediction of 'en_ex' using 'z_ex' will 
> essentially be a regression of 'ex on 'ex' - and thus basically
irrelevant??
> 
> I was thinking that ideally one ought to run a first-stage regression 
> of the form "reg  en z ex" (plus lags, as appropriate) and then use 
> the prediction to create the interaction term between the exogenous 
> variable, on the one hand, and the prediction of the endogenous 
> variable, on the other, before moving on with the second-stage 
> regression. Is this the wrong way of thinking about it? And, if not, 
> is there a way to implement this in Stata??

I don't quite understand your concern. That is indeed the equation I
proposed should be estimated, and the RHS endogenous variables (the
dependent variables in the nonexistent FSRs) are indeed en and en*ex. In
writing down a regression equation, we never assume that the regressors
are independent (presumably you mean uncorrelated, or orthogonal?) In a
textbook case, if they were, then you wouldn't need multiple regression.
So we imagine that ex, en and en_ex are by construction correlated to
some degree, as are z and z_ex.

If you were to run FSRs (by speciffying the -first- option in
-ivregress- or Baum-Schaffer-Stillman -ivreg2-, you can see them) we
would naturally expect the predicted values of those FSRs to be
correlated, just as en and en_ex are themselves correlated. So what?
If the variance of ex is very small, it is a lousy regressor, by the
simple logic of regression: the smaller the variance (or, properly,
variation) of the regressor, the larger its standard error, cet. par.
The same goes for the en variable; if it has a very small
variance/variability, how likely is it to explain much in the
relationship? 

I would emphatically NOT recommend "rolling your own" by running FSRs by
hand and then trying to do something sensible with them. That almost
surely will come to grief, and in worst case you will have entered the
land of the 'forbidden regression': e.g., see Mark's comments in

http://www.stata.com/statalist/archive/2005-05/msg00158.html

Cheers
Kit

Kit Baum   |   Boston College Economics & DIW Berlin   |
http://ideas.repec.org/e/pba1.html
                             An Introduction to Stata Programming  |
http://www.stata-press.com/books/isp.html
  An Introduction to Modern Econometrics Using Stata  |
http://www.stata-press.com/books/imeus.html


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