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


From   Babigumira Ronnie <rutaremwa_rb@yahoo.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: Subject: st: Does your academic discipline use logit regressions with interaction terms?
Date   Wed, 21 Aug 2002 02:02:24 -0700 (PDT)

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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