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

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: command or method for logistic quantile regression in Stata |

Date |
Sat, 18 Sep 2010 09:01:53 -0400 |

Doug Hess <douglasrhess@gmail.com>: The reference concerns fractional outcomes a la the fractional logit; see e.g. http://www.stata.com/support/faqs/stat/logit.html http://www.stata.com/meeting/12uk/Buis_proportions.pdf http://cohesion.rice.edu/Conferences/Econometrics/emplibrary/wooldridge.pdf http://www.stata.com/meeting/snasug08/abstracts.html#wooldridge and see also -locpr- on SSC for an alternative conditional mean model. Your second paragraph seems to describe breaking the RHS vars up into dummies capturing quantiles; this is feasible and quite different from your initial question. I differ on one minor point with Maarten: quantile regression can make sense for binary depvars, if we think each individual i has a probability p and we want to model how the quantiles of p vary with X, rather than how the mean of p varies with X. The issue is that you need variation within i to identify such a model, i.e. you need panel data. Here, quantile regression would be an alternative to -xtlogit-, not -logit-, and it turns out that -qreg- with individual heterogeneity is not so easy. I was predicting an -xtqreg- forthcoming in Stata back in May 2008, but I wouldn't hold my breath: http://www.stata.com/statalist/archive/2008-05/msg00957.html If you had sufficient panel data, you could easily estimate the "between" quantile regression by regressing the mean outcome by person on mean X by person with -qreg-; in such a case, you are back in the land of fractional outcomes. On Fri, Sep 17, 2010 at 5:58 PM, Doug Hess <douglasrhess@gmail.com> wrote: > Thanks, Maarten. This article mentions the concept of "logistic > quantile regression" for bounded dependent variables; would something > like this suitable for binary variables? Cite: "Logistic quantile > regression for bounded outcomes." Matteo Bottai, Bo Cai and Robert E. > McKeown. Statistics in medicine, Vol. 29, No. 2. (30 January 2010), > pp. 309-317; link: http://www.citeulike.org/user/guhjy/article/6306738 > > In other words, is there a good reason not to use the predicted > probabilities from a logit model to then chop the data up into > quantiles to see how some predictors variously impact across the > probability of the BDV hitting 1? I guess that can be done with > existing commands issued in steps, but I wasn't sure if there was code > just for this or caveats or special steps to take, or if it was just a > bad idea. > > Or can other transforms or link functions be used with binary > dependent variables to allow for quantile regression? > > In essence I'm asking the question that Abelson in his book > "Statistics as Principled Argument" encourages grad students to ask: > "Hey, can we do that?" > > -Doug > > > Date: Thu, 16 Sep 2010 07:39:28 +0000 (GMT) > From: Maarten buis <maartenbuis@yahoo.co.uk> > Subject: Re: st: command or method for logistic quantile regression in Stata > > - --- On Wed, 15/9/10, Doug Hess wrote: >> Are there any special commands (or steps if there is not a >> direct command) for running quantile regression for a binary >> dependent variable in Stata? > > I don't think quantile regression makes much sense for binary > variables. Any quantile of a binary regression can only be 0 > or 1 (maybe one arbitrary number in between if the order > statistic required for your quantile isn't an integer). So > whatever you do, you should end up with either a horizontal line > at 0 or 1, or a step function switching between 0 or 1. > > Hope this helps, > Maarten > * * 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**:**Re: st: command or method for logistic quantile regression in Stata***From:*Doug Hess <douglasrhess@gmail.com>

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