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From |
"alessia matano" <alexis.rtd@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: instrumental variable for quantile regression |

Date |
Tue, 27 May 2008 12:13:51 +0200 |

Dear Austin, I attempt to do the estimation with bootstrap....after time-demeaning my observation. I have just a doubt. It is true that bootstrap allows the cluster option, what i do not understand well is for what should i clustered my observations to allow correct standard errors after time-demenaing myself the observations. generally i clustered them not for individual, but for provinces where they live (cause of heteroskedasticity). However bootstrap either using the individual cluster or using the province ones does not work, saying that there are time repeated values within the panel, while when I tsset them it understands the panel structure. May you help me with this also? It works only when I cluster() with no variables inside. May you suggest me something? Also to read about? thank you very much alessia 2008/5/23 alessia matano <alexis.rtd@gmail.com>: > Many thanks for this suggestion also Austin. I will try to check how > it works. My sample is actually of 500000 observation for like 40000 > individuals. However I will try also to run the command with some > random subsample of my dataset (just for computational need). > For the question of instrumenting I will read the references you cite > and try to apply them using mata. > if you have something more to suggest me, it is welcome of course. > alessia > > 2008/5/23 Austin Nichols <austinnichols@gmail.com>: >> alessia matano <alexis.rtd@gmail.com>: >> I believe someone at Statacorp is working on an "xtqreg", but that >> won't be available in time to help you. qreg doesn't support cluster, >> but bootstrap does: >> >> http://www.stata.com/statalist/archive/2007-09/msg00147.html >> >> but you will want to run some simulations to see how your approach >> works in finite samples, I think. >> >> On Fri, May 23, 2008 at 6:14 AM, alessia matano <alexis.rtd@gmail.com> wrote: >>> Dear Austin and Brian. >>> >>> Thanks first for your answers. Austin I probably missunderstood the >>> answer of Brian in that previous message suggesting such a procedure, >>> I thought it was to solve this kind of problem. Anyway I should be >>> more explicity about the data I use. I have individual panel data >>> (with wages and a set of individual characteristics) and I matched >>> them with provincial variables whose I would like to estimate the >>> impact on differnet points of workers wage distribution. >>> >>> Hence I have two problems I'd like to solve. >>> - The first is that because of likely endogeneity I would like to >>> instrument my provincial variable and perform a quantile regression. >>> This is the reason why of my post. I thought that it could have been >>> possible to run a first stage and then correcting the standard errors >>> performing the qreg estimation. But it is not correct. Thank for this >>> >>> - The second. I would like to correct for individual unoberved >>> heterogeneity and hence to see if it is possible to estimate a >>> quantile fixed effects estimation. I know that also this question is >>> not easy to solve. I red another stata faq that suggests to use >>> xtdata, fe and then apply qreg clustering the standard errors with a >>> general sandwich formula (?!?). The last point I did not well >>> understood, and also qreg does not allow clustering options. If you >>> have something to advice also on this topic, many thanks. Below the >>> link of the related stata faq. >>> >>> http://www.stata.com/statalist/archive/2004-07/msg00926.html >>> >>> thank you again >>> alessia >> * >> * 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/ >> > * * 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/

**Follow-Ups**:**Re: st: instrumental variable for quantile regression***From:*"Austin Nichols" <austinnichols@gmail.com>

**References**:**st: instrumental variable for quantile regression***From:*"alessia matano" <alexis.rtd@gmail.com>

**Re: st: instrumental variable for quantile regression***From:*"alessia matano" <alexis.rtd@gmail.com>

**Re: st: instrumental variable for quantile regression***From:*"Austin Nichols" <austinnichols@gmail.com>

**Re: st: instrumental variable for quantile regression***From:*"alessia matano" <alexis.rtd@gmail.com>

**Re: st: instrumental variable for quantile regression***From:*"Austin Nichols" <austinnichols@gmail.com>

**Re: st: instrumental variable for quantile regression***From:*"alessia matano" <alexis.rtd@gmail.com>

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