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Re: st: -ivreg- in PolSci


From   Mark Schaffer <M.E.Schaffer@hw.ac.uk>
To   statalist@hsphsun2.harvard.edu, Clive Nicholas <Clive.Nicholas@newcastle.ac.uk>
Subject   Re: st: -ivreg- in PolSci
Date   Fri, 28 Nov 2003 22:46:28 +0000 (GMT)

Clive,

Quoting Clive Nicholas <Clive.Nicholas@newcastle.ac.uk>:

> All,
> 
> I don't wish to step on anybody's toes here, but this seems to me be
> a
> fascinating discussion. For my research, I was warned off using IVs
> in my
> model specifcations of district-level election outcomes: too messy
> and too
> involved, this professor said. Stick to straight time-series.
> Needless to
> say, I acquiesced.
> 
> The trouble is, one of the key variables that has emeraged in my
> early
> model testing is 'campaign intensity', using constituency spending
> as
> surrogates. But since (future) spending can be influenced as much
> by
> (previous) voting outcomes as the other way around, -ivreg- would
> appear
> to be ideal approach of choice to test out the possible endogenity
> of
> spending. Or is it? From what I've seen of it (Wooldridge and
> elsewhere),
> it looks very intimidating!

My 0.02: the ideas take a little getting used to, but once you get the 
gist of it, it's not too bad.  The hard part is actually finding 
instruments that are (a) valid, i.e., genuinely exogenous themselves, and
(b) relevant, i.e., correlated with what you're trying to instrument.  
Once you've found some candidates, you can test their validity and 
relevance, but sometimes there just aren't any there to be found.

If you want an introduction to IV and GMM in the Stata context, you might 
find the Stata Journal article I did with Kit Baum and Steve Stillman 
useful (Vol. 3, No. 1, March 2003).  If you don't get the SJ, you can 
still pick up the working paper version via RePEc at 
http://econpapers.hhs.se/paper/bocbocoec/545.htm

Cheers,
Mark

> 
> C.
> 
> > Steve,
> >
> > The answer to your questions is very nicely and succinctly
> discussed in
> > Wooldridge (2000), Econometric Analysis of Cross Section and Panel
> Data,
> > section 9.5, esp. pp. 236-7.
> >
> > The short answer is that you need to go down the route of your
> Option 1
> > and include xsquared as a second endogenous regressor.  If you do
> this,
> > you may need additional instruments.  One source of additional
> instruments
> > would be squares of some of the other exogenous variables.  A
> quite clever
> > idea is suggested by Wooldridge on p. 237.  It's similar to your
> Option 2
> > but with an important difference: instead of using xhatsquared as
> a
> > regressor in your second stage equation, use it as an
> *instrument*, i.e.,
> > estimate
> >
> > ivreg2 y q (x xsquared = z xhatsquared)
> >
> > In effect this adds a nonlinear function of your exogenous
> variables to
> > your instrument set.
> >
> > Your Option 2 is apparently a trap worthy of a special term,
> > namely "forbidden regression".  In Wooldridge's words, the
> mistake
> > behind "is in thinking that the linear projection of the square is
> the
> > square of the linear projection".  See the book for a detailed
> discussion.
> >
> > Cheers,
> > Mark
> >
> > Quoting "Morris, Stephen" <s.morris@imperial.ac.uk>:
> >
> >> Hi,
> >>
> >> Does anyone know of a way to run a 2SLS model in Stata where
> the
> >> endogenous RHS variable would ideally appear in a quadratic
> form?
> >>
> >> I am using -ivreg2- to find the effect of an independent variable
> x
> >> on a dependent variable y, where I believe that x and y will be
> >> simultaneously determined. I have what I think are a set of
> >> non-weak, orthogonal instruments for x, namely z. So, the command
> I
> >> use is:
> >>
> >> ivreg2 y q (x = z)
> >>
> >> q is a set of exogenous variables also thought to influence y.
> >>
> >> I have reason to believe that the true impact of x on y is
> >> non-linear, and I would ideally like to estimate a model
> including x
> >> and x squared. Given that x is simultaneously determined with y I
> am
> >> not sure how to proceed.
> >>
> >> Option 1:
> >>
> >> One approach would be to run –ivreg2- as normal and
> instrument
> >> both x and x squared. That is, to run:
> >>
> >> ivreg2 y q (x xsquared = z)
> >>
> >> This produces a set of results, but the sign and magnitude of
> the
> >> coefficients on x and x squared are counterintuitive. I think
> this
> >> might be because unless my first stage model is able to predict
> >> perfectly x and x squared (which it is not) I will not actually
> be
> >> modelling a quadratic form (i.e. the predicted value of x
> squared
> >> from the first stage regressions does not equal the square of
> the
> >> predicted value of x).
> >>
> >> Option 2:
> >>
> >> So, the other thing I thought to do was to estimate the first
> stage
> >> equation for x and compute the linear prediction (call this
> xhat).
> >> Then square these predictions (call this xhatsquared) and use
> these
> >> to measure the effects of x squared in my second stage:
> >>
> >> reg y q xhat xhatsquared
> >>
> >> The results appear to be more sensible, but I am not sure if
> the
> >> approach is valid.
> >>
> >> Any thoughts on which option to use, if either, would be
> greatly
> >> appreciated. I am using Stata version 8.2. I have previously
> >> searched the FAQ and the Statalist archives, and the question I
> pose
> >> is similar to one posted by Jim Shaw on 18 July, but with respect
> to
> >> non-linear RHS endogenous variables rather than non-linear RHS
> >> exogenous variables.
> >>
> >> Thanks very much.
> >>
> >> Steve
> >>
> >>
> >>
> >> *
> >> *   For searches and help try:
> >> *   http://www.stata.com/support/faqs/res/findit.html
> >> *   http://www.stata.com/support/statalist/faq
> >> *   http://www.ats.ucla.edu/stat/stata/
> >>
> >
> >
> >
> > Prof. Mark Schaffer
> > Director, CERT
> > Department of Economics
> > School of Management & Languages
> > Heriot-Watt University, Edinburgh EH14 4AS
> > tel +44-131-451-3494 / fax +44-131-451-3008
> > email: m.e.schaffer@hw.ac.uk
> > web: http://www.sml.hw.ac.uk/ecomes
> > ________________________________________________________________
> >
> > DISCLAIMER:
> >
> > This e-mail and any files transmitted with it are confidential
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> > whom it is addressed.  If you are not the intended recipient
> > you are prohibited from using any of the information contained
> > in this e-mail.  In such a case, please destroy all copies in
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> > Watt University does not accept liability or responsibility
> > for changes made to this e-mail after it was sent, or for
> > viruses transmitted through this e-mail.  Opinions, comments,
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> > ________________________________________________________________
> > *
> > *   For searches and help try:
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> >
> 
> 
> Yours,
> CLIVE NICHOLAS,
> Politics Building,
> School of Geography, Politics and Sociology,
> University of Newcastle-upon-Tyne,
> Newcastle-upon-Tyne,
> NE1 7RU,
> United Kingdom.
> *
> *   For searches and help try:
> *   http://www.stata.com/support/faqs/res/findit.html
> *   http://www.stata.com/support/statalist/faq
> *   http://www.ats.ucla.edu/stat/stata/
> 



Prof. Mark Schaffer
Director, CERT
Department of Economics
School of Management & Languages
Heriot-Watt University, Edinburgh EH14 4AS
tel +44-131-451-3494 / fax +44-131-451-3008
email: m.e.schaffer@hw.ac.uk
web: http://www.sml.hw.ac.uk/ecomes
________________________________________________________________

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Watt University does not accept liability or responsibility
for changes made to this e-mail after it was sent, or for
viruses transmitted through this e-mail.  Opinions, comments,
conclusions and other information in this e-mail that do not
relate to the official business of Heriot Watt University are
not endorsed by it.
________________________________________________________________
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