Re: st: RE: RE: RE: Reference group for categorical interactions

["Hussein, Mustafa (Mustafa Hussien)" <mhussei4@uthsc.edu>]

Thanks, Steve for Norton's article. ORs remain very widely used, especially in fields outside economics and strongly quantitative social sciences. 

Mustafa

On Sep 27, 2013, at 4:14 AM, "S.Jenkins@lse.ac.uk" <S.Jenkins@lse.ac.uk> wrote:

> The debate about the relative usefulness of odds ratios and marginal effects will run and run, and for some good reasons.
> 
> Nonetheless, for some strongly expressed views that are counter to Maarten's, and which I have some sympathy with, have a look at:
> 
> "Log Odds and Ends", by Edward C. Norton, NBER Working Paper No. 18252, http://www.nber.org/papers/w18252
> Inter alia, Norton states in his abstract " There is no one odds ratio ..."
> 
> Stephen
> ------------------
> Stephen P. Jenkins <s.jenkins@lse.ac.uk>
> ----------------------------------------------------------------------
> 
> Date: Thu, 26 Sep 2013 09:36:32 +0200
> From: Maarten Buis <maartenlbuis@gmail.com>
> Subject: Re: st: RE: RE: RE: Reference group for categorical interactions
> 
> On Thu, Sep 26, 2013 at 1:53 AM, Hussein, Mustafa wrote:
>> Though widely used, ORs mask the heterogeneity in the marginal effects across subjects, and their interpretation in the presence of interaction terms is not straightforward. I would suggest sticking to the marginal effects at the means, if that's meaningful, or estimate them at some relevant representative values for other covariates.
> 
> A different take on this issue is that a marginal effect is a linear
> model estimated on the results of a non-linear (logit) model. If you
> need a second model to interpret the results of your original model,
> then there is something wrong with your original model. The purpose of
> a model is to simplify what you have seen (your data) such that it is
> interpretable, and if you think you need to estimate a second model to
> interpret the results of your first (logit) model, then your first
> model is not doing what it is supposed to be doing.
> 
> I would recommend to stick to the interpretation of the model in terms
> of its natural parameters in their natural form as the main form of
> interpretation, marginal effects can play a useful role as a secondary
> interpretation. So you would need to choose your model such that its
> natural parameters correspond with what you and your audience are
> comfortable with: If you want risk differences you would estimate a
> linear probability model, if you want risk ratios you estimate a model
> with a log link (e.g. -poisson-), if you want odds ratios you estimate
> a logit.
> 
> It may be that you will find that a linear probability model or a
> Poisson model does not fit the data well, and you will need to move on
> to a logit model. That is a good thing: by estimating these models
> directly you can easily detect whether your model makes sense. If
> instead you had estimated it indirectly by first estimating a logit
> model and then estimating marginal effects, you probably would not
> have seen that the final model (the marginal effects _not_ the logit)
> does not fit the data.
> 
> When it comes to the interpretation of interaction terms in a logit model, see:
> M.L. Buis (2010) "Stata tip 87: Interpretation of interactions in
> non-linear models", The Stata Journal, 10(2), pp. 305-308.
> <http://www.maartenbuis.nl/publications/interactions.html>
> 
> Hope this helps,
> Maarten
> 
> - ---------------------------------
> Maarten L. Buis
> WZB
> Reichpietschufer 50
> 10785 Berlin
> Germany
> 
> http://www.maartenbuis.nl
> - ---------------------------------
> 
> 
> Please access the attached hyperlink for an important electronic communications disclaimer: http://lse.ac.uk/emailDisclaimer
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