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From | John Antonakis <John.Antonakis@unil.ch> |
To | statalist@hsphsun2.harvard.edu |
Subject | Re: st: GMM error (bug in Stata?) |
Date | Sat, 29 Oct 2011 18:56:08 +0200 |
Hi Stas: Thanks for your note.Yes; the main equations identical but that has not stopped gmm before; run the following:
clear set seed 123 set obs 1000 gen x = rnormal() gen z = rnormal() gen q = rnormal() gen y1 = x + z + q + rnormal() gen y2 = y1 + q + rnormal() gmm (eq1: y2 - {b1}*y1 - {b0}) /// (eq2: y2 - {c1}*y1 - {c0}), /// instruments(eq1: x) /// instruments(eq2: z) /// twostep winitial(unadjusted, indep) test [b1]_cons = [c1]_consThe goal of my estimation procedure is to make cross model comparison, where the model have different instruments ( and given the clustering I have, I want to have a generalized Hausman test hence the use of gmm). I want to show that the second stage estimates don't change when I change the instruments....it's a simulation study I am working on, hence the "strangeness".
So, it seems that I don't have an error in my initial code then? Best, J. __________________________________________ Prof. John Antonakis Faculty of Business and Economics Department of Organizational Behavior University of Lausanne Internef #618 CH-1015 Lausanne-Dorigny Switzerland Tel ++41 (0)21 692-3438 Fax ++41 (0)21 692-3305 http://www.hec.unil.ch/people/jantonakis Associate Editor The Leadership Quarterly __________________________________________ On 29.10.2011 18:42, Stas Kolenikov wrote:> On Sat, Oct 29, 2011 at 11:33 AM, John Antonakis <John.Antonakis@unil.ch> wrote:
>> Hi: >>>> I am trying to estimate "stacked" models using -gmm-. When I estimate the
>> two models separately, things work fine (see code on the bottom of my >> e-mail). However, when I run the models jointly, with the following >> code........... >> >> gmm ///>> (eq1: y - {b1}*x_style1 - {b2}*x_style2- {b3}*x_style3- {b4}*x_style4- /// >> {b5}*x_style5- {b6}*x_style6 - {b7}*x_style7- {b8}*x_style8- {b9}*x_style9-
>> ///>> {b10}*x_style10- {b11}*x_style11- {b12}*x_style12- {b13}*x_style13- {b0})
>> /// >>>> (eq2: y - {c1}*x_style1 - {c2}*x_style2- {c3}*x_style3- {c4}*x_style4- /// >> {c5}*x_style5- {c6}*x_style6 - {c7}*x_style7- {c8}*x_style8- {c9}*x_style9-
>> ///>> {c10}*x_style10- {c11}*x_style11- {c12}*x_style12- {c13}*x_style13- {c0}),
>> /// >>>> instruments(eq1: x_fe1 x_fe2 x_fe3 x_fe4 x_fe5 x_fe6 x_fe7 x_fe8 x_fe9 ///
>> x_fe10 x_fe11 x_fe12 x_fe13 ) /// >>>> instruments(eq2: x_clus1 x_clus2 x_clus3 x_clus4 x_clus5 x_clus6 x_clus7
>> /// >> x_clus8 x_clus9 x_clus10 x_clus11 x_clus12 x_clus13) /// >> >> twostep winitial(unadjusted, indep) vce(cluster lead_n) >> > John, are the main equations identical? That's a strange model to > estimate, then. At the very least, the orthogonality of residuals from > the first equation and from the second equations to the intercept > would imply identical moment conditions, and that would be enough for > GMM to break down. > * * 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/