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From |
Austin Nichols <austinnichols@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: ordered logistic regression with endogenous variable |

Date |
Thu, 11 Oct 2012 16:57:13 -0400 |

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! * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: ordered logistic regression with endogenous variable***From:*"Justina Fischer" <JAVFischer@gmx.de>

**References**:**Re: st: ordered logistic regression with endogenous variable***From:*"Justina Fischer" <JAVFischer@gmx.de>

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