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# Re: st: ordered logistic regression with endogenous variable

 From Austin Nichols To statalist@hsphsun2.harvard.edu Subject Re: st: ordered logistic regression with endogenous variable Date Thu, 11 Oct 2012 16:33:14 -0400

```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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