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From |
"Leda Inga" <ledainga@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: About the consequence of doing a two step estimation manually |

Date |
Tue, 10 Jun 2008 11:52:08 -0500 |

I'm really a beginner in Stata. I've read the help of gllamm and have no idea on how to set it up. I would be very greatfull if anybody could help me with this. Following the help, I think it would be something like this: gllamm Y Z C Csquared, link(logit) fam(binom) i(V001) V001: the cluster to which each individual belongs to in my data (DHS). pwi: the weights for each individual But how could I manage with the correlation between C and e (the error term of the latent variable Y*)?? 2008/6/10 jverkuilen <jverkuilen@gc.cuny.edu>: > Did you try setting it up in -gllamm-? > > -----Original Message----- > From: "Leda Inga" <ledainga@gmail.com> > To: statalist@hsphsun2.harvard.edu > Sent: 6/9/2008 7:53 PM > Subject: Re: st: About the consequence of doing a two step estimation manually > > Thank you very much. I definitely donīt want to get standard errors > that are too small, the problem is that I don't know how to it right. > > My model is like this: > > Y*= aZ + bC +bC^2 + e > > Y*>0, Y=1 > > Y*<0, Y=0 > > Z: exogenous regressors > > > As I mentioned before C is correlated with e. I have instruments (W) > but donīt know how to do a correct estimation. ivprobit doesn`t allow > for a square term. > > I asked before in statalist if this would be right, but got no answer: > > ivprobit Y Z (C Csquared=W Chatsquared) > > Chatsquare: square of predicted values of regressio of C on Z and W. > > > > > > 2008/6/9 David Greenberg <dg4@nyu.edu>: >> If you do the estimation m,anually by substituting the predicted value of a predictor into an equation for the second-stage estimation, the software will treat that predicted value as a true observed value, ignoring the fact that there is some uncertainty surrounding the prediction in the first stage. Consequently the second-stage estimation is going to produce estimates with standard errors that are too small. A question that one might ask is why you want to do this manually. David Greenberg, Sociology Department, New York University >> >> ----- Original Message ----- >> From: Leda Inga <ledainga@gmail.com> >> Date: Monday, June 9, 2008 7:19 pm >> Subject: st: About the consequence of doing a two step estimation manually >> To: statalist@hsphsun2.harvard.edu >> >> >>> Dear statalisters: >>> >>> I've tried to found a way to do a correct two step estimation but >>> haven't found an answer. I have an explanatory variable correlated >>> with the error term and that variable enters the model also with a >>> square term. I know that if the estimation is done manually the betas >>> are still consistent and that the problem is the standard errors are >>> not correctly calculated. What I would like to know is if the standard >>> errors are subestimated or overestimaded? The pvalue of one of the >>> regressors of my model is very high and would like to know if the true >>> pvalue is higher or lower. >>> >>> Thanks in advance. >>> * >>> * For searches and help try: >>> * http://www.stata.com/support/faqs/res/findit.html >>> * http://www.stata.com/support/statalist/faq >>> * http://www.ats.ucla.edu/stat/stata/ >> * >> * For searches and help try: >> * http://www.stata.com/support/faqs/res/findit.html >> * http://www.stata.com/support/statalist/faq >> * http://www.ats.ucla.edu/stat/stata/ >> > > * > * For searches and help try: > * http://www.stata.com/support/faqs/res/findit.html > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ > > > * > * For searches and help try: > * http://www.stata.com/support/faqs/res/findit.html > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ > * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**RE: st: About the consequence of doing a two step estimationmanually***From:*jverkuilen <jverkuilen@gc.cuny.edu>

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