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From |
"Martin Weiss" <martin.weiss1@gmx.de> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
st: RE: need help with heckprob in conjunction with ivprobit |

Date |
Tue, 4 Nov 2008 14:48:07 +0100 |

If all you want from -heckprob- are the first stage results, would it not be easier to use the respective -probit- instead of -heckprob- to get those predictions? ************* webuse school, clear heckprob private years logptax, sel(vote=years loginc logptax) nolog first *shortcut to your goal prob vote years loginc logptax ************* BTW, the first stage results in the -heckprob- are only displayed if the log for the maximization is on AND you specify the -first- option. Weirdly, when I turn the log off with the -nolog- option, -first- seems to be ignored... HTH Martin -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of mbaier Sent: Tuesday, November 04, 2008 12:19 PM To: statalist@hsphsun2.harvard.edu Subject: st: need help with heckprob in conjunction with ivprobit Dear statalisters, I am new to Stata and I am not sure if the estimation procedure below makes sense or if there are any pitfalls I may have ignored. Any help would be greatly appreciated. My overall goal is to estimate the propensity to cooperate (yes/no) subject to a few independent variables. 1) As first part of my estimation, I want to correct for selection bias, since only the innovators had to answer if they cooperate or not. I tried two ways, one is the procedure suggested according to http://www.stata.com/statalist/archive/2006-09/msg00772.html and the second one is doing a heckprob, more precisely: xi: heckprob coop xi.independent varlist, select (inno=xi.independent varlist), first In fact, I am not interested in the heckprob equation, but only in the selection equation, since I want to determine the Inverse Mill' s Ratio. After heckprob, I calculate the linear predictors of the selection equation predict psel, xbsel generate IMR= normalden(psel)/normal(pesel) I use the calculated values of IMR as additional regressor for the substantial equation in 2). I have two questions: Is there a difference if I use the heckprob or the procedure described in the link above (which was for a logit)? I understand that one is following a probit and the other one a logit distribution, but apart from this? As far as I can see from the results of the second equation, there seems to be no difference. Does it cause any problems if I only have categorial variables as regressors? 2) The second part, the substantial equation, is supposed to account for the selection bias of being an innovator or not AND the endogeneity of the R&D intensity as an IV. I consider R&D intensity as an endogneous variable, because theory says that the propensity to cooperate and R&D intensity are interdependent. Since cooperation is a dichotomous variable, I use xi: ivprobit coop xi.independent varlist IMR (R&D_int=instruments) The results of ivprobit show a significant Rho(-0.9), indicating that R&D_int is in fact endogeneous (as far as I understand). Unfortunately, not only the coefficient of the IMR turnes out to be totally insignificant, but many regressors have become insignificant, too. What does it mean and how can I solve this problem? I suspect that the insignificance of the IMR coefficient may be caused by the fact that the LR test of indep.equ. in the preceding heckprobit equation was Prob>chi2=0.1461, but I am not sure if this tips the scales. I know these are a lot of questions, but maybe someone could show me the right path. Maybe there are further aspects which I forgot to consider. Thanks a lot, melanie - melanie baier c-lab * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/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/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: need help with heckprob in conjunction with ivprobit***From:*mbaier <melanie.baier@c-lab.de>

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