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Re: st: ordered logistic regression with endogenous variable


From   "Justina Fischer" <JAVFischer@gmx.de>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: ordered logistic regression with endogenous variable
Date   Thu, 11 Oct 2012 22:50:50 +0200

Hi Austin,

I was always wondering whether the following two-step-procedure would be a feasible solution:

1) assuming a valid instrument at hand, run the auxiliary (first stage) regression by hand (OLS) and predict values of the instrumented

2) run the second stage regression with ologit, using the predicted values as independent variable, _but_ bootstrap standard errors ?

Or does the non-lineratity of the model pose a problem when using this approach?

Thx

Justina
-------- Original-Nachricht --------
> Datum: Thu, 11 Oct 2012 16:33:14 -0400
> Von: Austin Nichols <austinnichols@gmail.com>
> An: statalist@hsphsun2.harvard.edu
> Betreff: Re: st: ordered logistic regression with endogenous variable

> Anat (Manes) Tchetchik <anatmanes@gmail.com>:
> You can also recast your ordinal variable as ranging from 0 to 1
> with outcomes in {0,.25,.5,.75,1} and use a fractional model
> as described in e.g.
> "Inference for partial effects in nonlinear panel-data models using Stata"
> by Jeffrey Wooldridge, linked from
> http://www.stata.com/meeting/snasug08/abstracts.html
> 
> On Thu, Oct 11, 2012 at 2:04 PM, Anat (Manes) Tchetchik
> <anatmanes@gmail.com> wrote:
> > Thanks Jay,
> > Actually this is not our main model (rather it is an "auxiliary" one
> > aiming to validate some relations) our main model is a count one with
> > IVs.
> > I'm not sure I understood what did you mean by: problems with the
> > residuals, I ran the  IVregress and received the following stats.
> > (with some of the coefficients signif. as expected )
> > Instrumental variables (2SLS) regression      Number of obs =  603
> >                                                        Wald chi2(14) = 
> 169.62
> >                                                        Prob > chi2   = 
> 0.0000
> >                                                        R-squared     = 
> 0.3208
> >                                                        Root MSE      = 
> 1.0537
> > Anat
> >
> >
> > On Thu, Oct 11, 2012 at 7:10 PM, JVerkuilen (Gmail)
> > <jvverkuilen@gmail.com> wrote:
> >> On Thu, Oct 11, 2012 at 12:38 PM, Anat (Manes) Tchetchik
> >> <anatmanes@gmail.com> wrote:
> >>> Hi Jay, It is a 5 categories var. however not symmetric (i.e. value 1
> >>> appears 10%, 2 appears 11%, 3- 22% , 4-27% and 5- 31%) so it doesn't
> >>> fit into the IV estimator, shell I run gmm?
> >>
> >> That's not too bad in terms of skew, but you could have important
> >> subgroups be skewed, so if for instance males are really positive on
> >> the measure and females are really negative, the overall measure might
> >> appear symmetric but not be at the level you want to analyze.
> >>
> >> You will get some attenuation of statistical power due to the coarse
> >> response scale. You can try running an ordinary estimator, but if you
> >> notice problems with the residuals, I'd switch to -gllamm- for an
> >> ordinal probit model, or -gmm-. Specifying the model for either is not
> >> a trivial matter, though, so I totally understand the desire to work
> >> with a linear estimator!
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