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From |
"Lachenbruch, Peter" <Peter.Lachenbruch@oregonstate.edu> |

To |
"'statalist@hsphsun2.harvard.edu'" <statalist@hsphsun2.harvard.edu> |

Subject |
RE: st: RE: RE: eivreg and deming |

Date |
Wed, 2 Jun 2010 08:38:40 -0700 |

Thanks for that. Those of us who don't live in the exo-endo- world need reminding every now and then. Tony Peter A. Lachenbruch Department of Public Health Oregon State University Corvallis, OR 97330 Phone: 541-737-3832 FAX: 541-737-4001 -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of John Antonakis Sent: Tuesday, June 01, 2010 2:17 PM To: statalist@hsphsun2.harvard.edu Subject: Re: st: RE: RE: eivreg and deming Sorry about that....for the benefit of those who don't know the terms, by endogenous, I mean that the modeled independent variable correlates with the error term of the y equation. By exogenous I mean randomly varying (and does not correlate with the error term). Measurement error is a special case of endogeneity where x is actually exogenous; however, because of measurement error it correlates with the error term (thus rendering it endogenous). For those who wish to know more, here is a snippet from one of my papers where I explain this in more detail: Suppose we intend to estimate the following model, where we intend to observe is a latent variable, x*: y=b0+b1x*+e However, instead of observing x*, which is exogenous and a theoretically "pure" or latent construct, we observe instead a not-so-perfect indicator or proxy of x*, which we call x (assume that x* is the IQ of leader i). This indicator consists of the true component (x*) in addition to an error term (u) as follows (see Cameron & Trivedi, 2005; Maddala, 1977): x=x*+u, or x*=x-u Now substituting the above into the first equation gives: y=b0+b1(x-u)+e Expanding and rearranging the terms gives: y=b0+b1x+(e-b1u) As is evident, the coefficient of x will be inconsistent given that the full error term, which now includes measurement error too, is correlated with x. Note that measurement error in the y variable does not bias coefficients and is not an issue because it is absorbed in the error term of the regression model. Variables that are correlated with the problematically-measured variable will also be affected if the bias is not removed from x. By constraining the residual to (1-reliability)*Variance of x (Bollen, 1989), we can purge x from endogeneity bias. Ref: Bollen, K. A. (1989). Structural equations with latent variables. New York: Wiley. Cameron, A. C., & Trivedi, P. K. (2005). Microeconometrics: Methods and applications. New York: Cambridge University Press. Maddala, G. S. (1977). Econometrics. New York: McGraw-Hill. Best, J. ____________________________________________________ Prof. John Antonakis, Associate Dean 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 Faculty page: http://www.hec.unil.ch/people/jantonakis Personal page: http://www.hec.unil.ch/jantonakis ____________________________________________________ On 01.06.2010 22:23, Nick Cox wrote: > Here as elsewhere I note that the exogenous-endogenous terminology is > one widely used by economists and not one that is natural or even > familiar to many of us outside economics. That aside, I do agree that > -eivreg- is a method not requiring instrumental variables which could be > used so long as you have a good idea about reliability. > > Nick > n.j.cox@durham.ac.uk > > John Antonakis > > One example where eivreg is perfectly legitimate to use: IQ is mostly > exogenous (determined by genes); so, if we have a non-so-perfect proxy > of IQ, we can estimate its reliability (empirically via test-retest or > via internal consistency) and thus "purge" the endogeneity bias due to > measurement error. This is much easier to do and more defensible than > trying to instrument IQ. I would be hard pressed to find a good > instrument for IQ. > > On 01.06.2010 19:43, Nick Cox wrote: > > >> Compared with what? is a flip but nevertheless I suggest also a fair >> answer. >> >> I can't comment on Tony's specifics here -- as there aren't any! -- >> > but > >> I guess that many people feel queasy in this territory because >> > deciding > >> on a proper treatment of situations in which all variables are subject >> to error is very demanding. There are so many things to be specified >> about error structure. >> >> StataCorp's own feelings appear mixed too: there is a bundle of good >> stuff at http://www.stata.com/merror that is semi-official (my >> description not theirs!). >> >> By the way, many economists and econometricians seem fixated on using >> instrumental variables in this situation, but such methods don't >> > exhaust > >> the possibilities. >> >> Nick >> n.j.cox@durham.ac.uk >> >> Lachenbruch, Peter >> >> At a seminar not long ago, an eminent statistician commented that EIV >> was not very useful and led to more problems (he didn't specify what >> they were) that it was worth. Anyone else have similar experience? >> >> Risto.Herrala@bof.fi >> >> I need to do errors in variables regression, where the errors are >> heteroscedastic. A Stata user has programmed a 'deming' ado -file for >> this purpose. Does anyone have experience of its use? >> >> * >> * 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/ > * * 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/

**Follow-Ups**:**AW: st: RE: RE: eivreg and deming***From:*"Martin Weiss" <martin.weiss1@gmx.de>

**References**:**st: eivreg and deming***From:*<Risto.Herrala@bof.fi>

**st: RE: eivreg and deming***From:*"Lachenbruch, Peter" <Peter.Lachenbruch@oregonstate.edu>

**st: RE: RE: eivreg and deming***From:*"Nick Cox" <n.j.cox@durham.ac.uk>

**Re: st: RE: RE: eivreg and deming***From:*John Antonakis <john.antonakis@unil.ch>

**RE: st: RE: RE: eivreg and deming***From:*"Nick Cox" <n.j.cox@durham.ac.uk>

**Re: st: RE: RE: eivreg and deming***From:*John Antonakis <john.antonakis@unil.ch>

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