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Re: st: ordered logistic regression with endogenous variable


From   "Anat (Manes) Tchetchik" <anatmanes@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: ordered logistic regression with endogenous variable
Date   Thu, 11 Oct 2012 22:36:40 +0200

Okay! I'll try this path as well, Thanks much!

On Thu, Oct 11, 2012 at 10:33 PM, Austin Nichols
<austinnichols@gmail.com> wrote:
> 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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-- 
Anat Tchetchik, PhD
Department of Hotel and Tourism Management
Guilford Glazer Faculty of Business and Management
Ben-Gurion University of the Negev
P.O.Box: 653
Beer-Sheva, Israel, 84105

E-mail:       anat@som.bgu.ac.il
Phone         972-(0)8-6479735
Fax:           972-(0)8-6472920
Web:          http://cmsprod.bgu.ac.il/Eng/som/hotelmanage/Staff/Academic/ChechikA.htm
*
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