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From |
"Stas Kolenikov" <skolenik@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: rcal, measurement error model question |

Date |
Fri, 12 Dec 2008 10:31:46 -0600 |

Richard, As with most measurement error models, the first question is whether the postulated relation holds between the variables measured with error, or between the "ideal" unobservable variables. You might want to clarify that. There might be identification issues, too: if you treat this as an econometric simultaneous equations model, it is identified as a triangular/recursive system if everything is perfectly observable. I am not sure how those identification rules work with measurement error models. Models with latent variables (and nothing observed without error) can be fit using -gllamm- (see another paper in that Stata Journal special issue on measurement error models, http://www.stata-journal.com/sjpdf.html?articlenum=st0052). A direct application might not be terribly helpful since -gllamm- will try to model the error variance(s) marginally, and you already know them. This can probably be circumvented by specifying fixed loadings equal to sqrt(v1) and sqrt(v2), and the fixed variance of the error terms equal to 1. Still -gllamm- would overlay an assumption of the (model) normality for u1 and u2, which you may or may not like. Given your position, I would suspect you have access to the raw survey data. May be you can try and fit the model using those directly? Otherwise, your (small area estimation?) problem might just be an exercise in matrix algebra if none of the pre-canned routines are directly applicable. And you would need to bring the covariances between u1 and u2 (model covariance) as well as the (design-based?) covariances between e1 and e2 if those are estimable/applicable. So unless I am imagining things (and I well might), things are more complicated than what you said. If your model is a direct translation of something from Sarndal's yellow book, just give the reference, we'll see if we can figure it out from there :)). On 12/11/08, Richard Valliant <rvalliant@survey.umd.edu> wrote: > I'm a new user who is trying to fit a simple measurement error model to > a set of estimates from two independent surveys. The surveys are > measuring the same things. My data look like > > (e1, v1) = set of 30 estimates and their variances from survey 1 > (e2, v2) = set of 30 estimates and their variances from survey 2 > > The model I want to fit is basic: > e1 = a + b*e2 + u1 > e2 = E2 + u2 (u1 and u2 are the model errors, E2 is E(e2) ) > > I've tried: > mkmat v2 > mat D = diag(v2) > rcal (e1) (w: e2), suuinit(D) > > This gives "invalid syntax". If I put some arbitrary variable x in the > model (which I don't want), this works: > rcal (e1=x) (w: e2), suuinit(D) > > But rcal apparently does not allow aweights to account for v1 = > var(e1). > Is there a way to use rcal or some other procedure to fit the model > above, accounting for the fact that I have (1) estimates of variance for > both e1 and e2 and (2) no covariates measured without error to put in > the model? -- Stas Kolenikov, also found at http://stas.kolenikov.name Small print: I use this email account for mailing lists only. * * 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: rcal, measurement error model question***From:*Richard Valliant <rvalliant@survey.umd.edu>

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