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From |
Steve Samuels <sjsamuels@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Stata implementation of difference-in-differences with binary outcomes |

Date |
Fri, 16 Apr 2010 11:14:12 -0400 |

I agree with Maarten. "(possibly with the -vce(robust)- " I'd say "necessarily, " or just use the "robust" option, in order to assure correct standard errors and tests. In my experience, a the estimated DiDs and their CIs do not have boundary problems: the possible range is -2 to +2, with the average usually close to the middle. Steve On Fri, Apr 16, 2010 at 10:57 AM, Maarten buis <maartenbuis@yahoo.co.uk> wrote: > --- On Fri, 16/4/10, C Engelbrecht wrote: >> But what if the outcome variable is binary? How should I >> model the difference of two latent variables, as is the >> case in Probit / Logit? The usual DID is based on >> differencing Y across these groups, but what should we >> do now that we only have a latent Y*? > > Difference in difference is all about getting at a causal > effect, which is usually difined as a difference in > averages. This also exists and is meaningful when the > dependent variable is binary, that is the risk difference. > You can calculate it using a linear probability model, > which is just a fancy name of using -regress- on a binary > variable (possibly with the -vce(robust)- option. > > There is often some uneasyness in specifying "the effect" > as linear in the probability metric, as that can > eventually lead to predictions outside the range [0, 1]. > However, if you define the effect interms of odds ratios > or probit coefficients, you won't get the causal effects > either, see for example: Mood 2010, Allison 1999, or > Neuhaus and Jewell 1993. > > So my guess would be that the linear probability model > is in this case the lesser of two evils. > > Hope this helps, > Maarten > > Allison, Paul D. 1999. "Comparing Logit and Probit > Coefficients Across Groups." Sociological Methods & > Research 28:186–208. > > Mood, Carina. 2010. "Logistic regression: Why we cannot > do what we think we can do, and what we can do about > it." European Sociological Review 26:67–82. > > Neuhaus, John M. and Nicholas P. Jewell. 1993. "A > Geometric Approach to Assess Bias Due to Omited > Covariates in Generalized Linear Models." Biometrika > 80:807–815. > > -------------------------- > Maarten L. Buis > Institut fuer Soziologie > Universitaet Tuebingen > Wilhelmstrasse 36 > 72074 Tuebingen > Germany > > http://www.maartenbuis.nl > -------------------------- > > > > > > > * > * 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/ > -- Steven Samuels sjsamuels@gmail.com 18 Cantine's Island Saugerties NY 12477 USA Voice: 845-246-0774 Fax: 206-202-4783 * * 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/

**Follow-Ups**:**Re: st: Stata implementation of difference-in-differences with binary outcomes***From:*Nils Braakmann <nilsbraakmann@googlemail.com>

**References**:**st: Stata implementation of difference-in-differences with binary outcomes***From:*C Engelbrecht <cngelbrecht@gmail.com>

**Re: st: Stata implementation of difference-in-differences with binary outcomes***From:*Maarten buis <maartenbuis@yahoo.co.uk>

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