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# Re: st: ordered logistic regression with endogenous variable

 From "Justina Fischer" To statalist@hsphsun2.harvard.edu Subject Re: st: ordered logistic regression with endogenous variable Date Thu, 11 Oct 2012 23:33:18 +0200

```okay, thanks, in the examples based on (linear) OLS/IV the non-correlation of the error term with the instrument z leads to consistency of the estimator.

yep, it would be nice to see some simulation results for the nonlinear case and your proposal, that would really be very practical.

-------- Original-Nachricht --------
> Datum: Thu, 11 Oct 2012 16:57:13 -0400
> Von: Austin Nichols <austinnichols@gmail.com>
> An: statalist@hsphsun2.harvard.edu
> Betreff: Re: st: ordered logistic regression with endogenous variable

> Justina Fischer <JAVFischer@gmx.de>:
> Typically, using predicted values in a nonlinear second stage does not
> result in a consistent estimator, but including residuals together
> with endog variables can.  Some kind of control function approach is
> typically feasible--I would want to see some simulation evidence on
> small-sample performance before I trusted any conclusions about
> consistency though...
>
> On Thu, Oct 11, 2012 at 4:50 PM, Justina Fischer <JAVFischer@gmx.de>
> wrote:
> > 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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```

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