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RE: Subject: st: Does your academic discipline use logit regressions with interaction terms?


From   "Javier Escobal" <jescobal@grade.org.pe>
To   <statalist@hsphsun2.harvard.edu>
Subject   RE: Subject: st: Does your academic discipline use logit regressions with interaction terms?
Date   Wed, 21 Aug 2002 11:14:22 -0500

following this thread, this is a question for Edward Norton:

Have you been able to develop an STATA program to construct the "true"
interaction effects and their standard errors as discussed in your
paper?

regards

Javier

> -----Mensaje original-----
> De: owner-statalist@hsphsun2.harvard.edu
> [mailto:owner-statalist@hsphsun2.harvard.edu] En nombre de
> Babigumira Ronnie
> Enviado el: Miércoles, 21 de Agosto de 2002 04:02 a.m.
> Para: statalist@hsphsun2.harvard.edu
> Asunto: Re: Subject: st: Does your academic discipline use
> logit regressions with interaction terms?
>
>
> Save it to your hard disc first and then try to open it.
> Should work fine
>
> Roni
> --- Owen Abbe <OABBE@capc.umd.edu> wrote:
> > When I try to open the file
> (http://www.unc.edu/the/the002.pdf) using
> > Adobe Acrobat 5.0, I get an error and the document does not load.
> >
> > Owen
> >
> >
> >
> > Owen Abbe, Research Fellow
> > Center for American Politics and Citizenship
> > University of Maryland
> > 1108 Tawes Building
> > College Park, Maryland 20742
> > 301-405-9722 (Tel)
> > 301-314-2532 (Fax)
> > www.capc.umd.edu
> >
> > >>> Edward_Norton@unc.edu 08/20/02 03:53PM >>>
> > Although applied economists often estimate interaction
> terms to infer
> > how the effect of one independent variable on the dependent
> variable
> > depends on the magnitude of another independent
> > variable, most researchers misinterpret the coefficient in nonlinear
> > models.  The magnitude of the
> > interaction effect does not equal the marginal effect of the
> > interaction term, can be of opposite
> > sign, and its statistical significance is not calculated by standard
> > software.  I have written a
> > paper with Chunrong Ai called "Interaction terms in
> nonlinear models"
> > in which we present the correct
> > way to estimate the magnitude and standard errors of the interaction
> > effect in nonlinear models,
> > including the widely used log transformation model with
> unknown error
> > distribution.
> >
> > You can find a copy of this working paper at
> > www.unc.edu/the/THE_WPS.htm
> >
> > Edward Norton
> > Associate Professor
> > UNC at Chapel Hill
> >
> >
> > > ------------------------------
> > >
> > > Date: Fri, 16 Aug 2002 15:11:57 -0400
> > > From: epowers@moore.sc.edu
> > > Subject: st: Does your academic discipline use logit
> regressions with
> > interaction terms?
> > >
> > > Hi, does your academic discipline typically use logit (or probit)
> > > regressions with interaction terms?  If so, then you may
> be able to
> > help
> > > me.
> > >
> > > I am an academic in finance.  There is a branch of research on
> > > management turnover in the finance literature that analyzes the
> > > sensitivity of turnover to performance, i.e. how badly does a firm
> > need
> > > to perform before the CEO is asked to leave (a
> particularly timely
> > > question these days!)  Often, the emphasis is on comparing two
> > > "types" of firms to see when turnover is most sensitive to
> > performance.
> > > For example, firms might be sorted into two groups based on
> > > characteristics of the board of directors, the people responsible
> > for
> > > supervising the CEO.
> > >
> > > In general, participants in this literature estimate logit
> > regressions
> > > where turnover=f(performance, type, type*performance,
> controls).  In
> > > general, researchers focus on the estimated coefficient for the
> > > interaction term of type*performance.  Unfortunately, the
> estimated
> > > coefficient for the interaction term (and its statistical
> > significance)
> > > depends not just on the true underlying sensitivity of
> turnover to
> > > performance, but on the difference in the average likelihood of
> > > turnover between the two types of firms. In general
> terms, this is
> > > because the estimate coefficient is the log of the odds
> ratio which
> > > depends on the underlying level of the odds.
> > >
> > > In any event, I have written a short note which uses
> simulations to
> > > illustrate this problem.  While it is a subtle point, it
> is really a
> > very basic
> > > point.  I would be incredibly surprised if it has not
> been addressed
> > in
> > > the literature of a different field.  That is where you
> can come in:
> > >
> > > 1) Is that particular form of logit or probit regression
> typical in
> > your
> > > field?  If so, can you give me some references?
> > >
> > > 2) Have you seen this statistical issue addressed in a paper,
> > textbook,
> > > etc? If so, can you give me some references.
> > >
> > > Thanks for any help that you can offer.
> > >
> > > Sincerely
> > >  Eric A. Powers
> > > Assistant Professor of Finance
> > > The Moore School of Business
> > > University of South Carolina
> > > Columbia SC, 29208
> >
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