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

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: ivpois with a binary endogenous predictor |

Date |
Sat, 22 Sep 2012 16:04:03 -0400 |

Dimitriy V. Masterov <dvmaster@gmail.com>: First, the blog post you reference ends with a reference to the presentation that inspired it: http://www.stata.com/meeting/boston10/boston10_nichols.pdf so you may want to start there. When you regress ln(y) on X, and you observe cases where y==0 or y<0, not only are you using the absurd model that E[ln(y)|X] is linear (impossible since ln y is undefined in sample, so the mean is undefined) instead of assuming that ln(E[(y)|X]) is linear (not impossible), but you are dropping cases with y<=0, and selecting on the dependent variable is not going to give unbiased estimates, no matter how good your instrument is! Finally, the coefs of -2.4 and -4.7 translate into a 90% and 99% reduction, respectively. Always think about what the corresponding positive coef would mean when translating these percentages in your head: a 2.4 coef implies levels 11 times higher, or 1000 percent higher. If a coef of 2.4 implies outcomes 11 times higher, a coef of -2.4 has to imply outcomes 1/11 as high. On Fri, Sep 21, 2012 at 9:26 PM, Dimitriy V. Masterov <dvmaster@gmail.com> wrote: > Thanks for the quick reply, Austin. I have 3 follow-up questions. I > tried the following command: > > ivpois y a b c, endog(x) exog(z) > > The coefficient on the binary endogenous variable x is -2.4. That's > the expected sign (compared to non-IV poisson), but the magnitude > seems too small. Using the dummy elasticity calculation for x going > from 0 to 1, -2.4 translates into an (exp(-2.4)-1) = 0.9% reduction in > y. Is that interpretation correct? > > I also tried this specification with -ivreg2- and ln(y), which gives > me a coefficient of -4.7, which is almost -1%. Are there any other > methods I can shoehorn this into to check my estimate? > > In his Stata Blog post*, William Gould wrote that that Poisson > regression with the Huber/White/Sandwich linearized estimator of > variance is a permissible alternative to log linear regression. Can > ivpois incorporate this? It does not let you specify vce(robust). > > * http://blog.stata.com/2011/08/22/use-poisson-rather-than-regress-tell-a-friend/ * * 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: ivpois with a binary endogenous predictor***From:*"Dimitriy V. Masterov" <dvmaster@gmail.com>

**Re: st: ivpois with a binary endogenous predictor***From:*Austin Nichols <austinnichols@gmail.com>

**Re: st: ivpois with a binary endogenous predictor***From:*"Dimitriy V. Masterov" <dvmaster@gmail.com>

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