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

To |
statalist@hsphsun2.harvard.edu |

Subject |
st: More about -ivpois- on SSC |

Date |
Mon, 12 Nov 2007 15:32:12 -0500 |

Several interlocutors have asked about the availability of slides from the Nov 2 talk by Bill Gould and David Drukker, which I intimated to the list was the bee's knees, or at least a useful demonstration of how easy it was to use Mata to program a new estimator. I hope Bill and David will post the slides from that talk on stata.com, since I think it would provide a tremendous incentive for folks to buy Stata 10, but if they don't, I can write up a different version of that demonstration and make it available. I don't think I can simply scan their handout and post it on the web, however. Another correspondent observed that "one can set up the moment conditions with an additive error or a multiplicative error (Mullahy, 1997)" and asked which version of the moment conditions were used. The answer is the latter (more details below). He also asked about panel models (which are not available from -ivpois- but I agree would be a good extension to pursue, though one can include dummy variables to get a FE estimator) and about cluster-robust SEs. Given that the SEs are supplied by -bootstrap-, the cluster() option may be specified. Here is a silly example: ssc inst ivpois sysuse auto, clear g manuf=word(make,1) ivpois mpg wei, exog(turn) endog(disp) cl(manuf) On the moment conditions, the additive form posits that y=exp(xb)+u and gives moment conditions of the form z(y-exp(xb))=0, whereas the multiplicative form posits y=exp(xb)u and gives moment conditions of the form z(y-exp(xb)/exp(xb)))=z(y*exp(-xb)-1))=0 for excluded instruments z satisfying E(z'u)=0. Angrist (2001) shows that in a model with endogenous binary treatment and a binary instrument, the latter procedure (assuming a multiplicative error) estimates a proportional local average treatment effect (LATE) parameter in models with no covariates. The latter is also more intuitively appealing and congruent with -poisson- and -glm-, and the assumption can be rewritten y=exp(xb)u=exp(xb)*exp(v)=exp(xb+v) so ln(y)=xb+v if y!=0 to provide the natural link to OLS. Windmeijer (2006) has a very useful discussion and further models that someone should implement in Mata. Maybe me. Angrist, Joshua D., 2001. "Estimation of limited dependent variable models with dummy endogenous regressors: simple strategies for empirical practice." Journal of Business and Economic Statistics, 19: 2-16. Mullahy, John. 1997. "Instrumental-Variable Estimation of Count Data Models: Applications to Models of Cigarette Smoking Behavior." The Review of Economics and Statistics, 79(4): 586-593. Windmeijer, Frank. 2006. "GMM for Panel Count Data Models." Discussion Paper No. 06/591, Department of Economics, University of Bristol. * * 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/

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