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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 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! > * > * 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/ * * 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/

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

**Re: st: ordered logistic regression with endogenous variable***From:*Austin Nichols <austinnichols@gmail.com>

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