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From |
Guy Grossman <guygrossman1@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Modeling proportion with endogenous treatment indicator |

Date |
Sat, 11 Sep 2010 09:53:48 -0400 |

Thanks for the recommendation. I just took a look. the beta coefficient from ssm and cmp are very different for the dnogenous treatment indicator (about half for cmp). On Sat, Sep 11, 2010 at 9:36 AM, Martin Weiss <martin.weiss1@gmx.de> wrote: > > <> > > > Have you looked at > > ************* > ssc d cmp > ************* > > ? > > HTH > Martin > > > -----Original Message----- > From: owner-statalist@hsphsun2.harvard.edu > [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Guy Grossman > Sent: Samstag, 11. September 2010 15:24 > To: statalist@hsphsun2.harvard.edu > Subject: Re: st: Modeling proportion with endogenous treatment indicator > > Thanks - this is helpful! > > May I follow up with another question - how will the response change > when Yij is binary? treatreg and iverg2 assume continuous dependent > variable and ivprobit assumes continuous endogenous regressor - so > none would be appropriate. > > By contrast ssm (wrapper for gllamm which is a user-written program) > can fit an IV model with endogenous binary treatment and binary > response. however it does not seem to allow clustering SEs at the > group level. > > Advice would be highly appreciated! > regards, > Guy > > On Fri, Sep 10, 2010 at 5:33 PM, Austin Nichols <austinnichols@gmail.com> > wrote: >> Guy Grossman <guygrossman1@gmail.com>: >> I would start with -ivreg2- (on SSC) and use robust SEs (or >> cluster-robust); the >> coefficients will certainly be easy to interpret as dp/dX. But see >> http://www.stata.com/meeting/snasug08/abstracts.html#wooldridge >> for panel models with fractional outcomes and instruments. >> >> On Fri, Sep 10, 2010 at 5:25 PM, Guy Grossman <guygrossman1@gmail.com> > wrote: >>> Dear Stata list - >>> >>> I am using Stata 10.1 on Mac and am seeking advice about the best way >>> to fit the following model: >>> >>> Yij = bo + b1 * Tj + b2 * Ci + b3 * Cj + eij + ej >>> >>> Tj = v0 + v1 * Zj + epsilon j, where... >>> >>> >>> Yij = the dependent variable is a proportion (0<Yij <1), for person i >>> from group j. >>> >>> Zj = encouragement to take up one of two types of treatment - applied >>> at the group level j . Zj is binary: groups are either encouraged to >>> take up Zj=0 or Zj=1. >>> >>> Tj = treatment take up - takes place at the group level j. Tj is >>> binary (Tj=0 or Tj =1). Take up rates are about 80% for both >>> treatments. >>> >>> Ci = control variables at the individual level i >>> >>> Cj = control variables at the group level j >>> >>> The idea is to apply an encouragement research design, using Zj as an >>> instrumental variable for Tj. >>> >>> Given the nature of the dependent variable (proportion), my question >>> is how is it best to fit the model in Stata. In the past I have used >>> the glm command with link(logic) and family (bin) to fit a model with >>> a dependent variable that was a proportion, but the independent >>> variables were all exogenous. Is there a way to fit a glm with an >>> endogenous independent variable? Is it possible to use treatreg with >>> DV which is a proportion? >>> >>> I look forward for your astute recommendations. >>> Thanks! >>> Guy >> >> * >> * 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/ > > > * > * 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/ > -- Guy Grossman Department of Political Science Columbia University * * 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: Modeling proportion with endogenous treatment indicator***From:*Guy Grossman <guygrossman1@gmail.com>

**Re: st: Modeling proportion with endogenous treatment indicator***From:*Austin Nichols <austinnichols@gmail.com>

**Re: st: Modeling proportion with endogenous treatment indicator***From:*Guy Grossman <guygrossman1@gmail.com>

**RE: st: Modeling proportion with endogenous treatment indicator***From:*"Martin Weiss" <martin.weiss1@gmx.de>

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