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

To |
statalist@hsphsun2.harvard.edu |

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

Date |
Fri, 12 Dec 2008 10:18:34 -0500 |

Richard Valliant <rvalliant@survey.umd.edu>: I'm pretty sure -rcal- does not do what you want, which I think is regressing e1 on e2 while specifying the measurement error variance for each observation on both e2 and e1. It seems from the paper I reference that -rcal- will only use the first element of your diagonal matrix as the assumed error variance for e2, which you can see directly by doing something like: clear drawnorm e1 v1 e2 v2, n(30) replace v1=exp(v1/10) replace v2=exp(v2/10) mkmat v2 mat D = diag(v2) mat d=D[1,1] rcal (e1=) (w: e2), suuinit(d) rcal (e1=) (w: e2), suuinit(D) Maybe something like this works for your example (but I hope others on Statalist will weigh in): su e2 loc e2var=r(Var) su v2 loc v2var=r(mean) loc r=(`e2var'-`v2var')/`e2var' eivreg e1 e2 [aw=v1], r(e2 `r') but note that above I am making no use of the individual-observation-specific error variances you have. On Thu, Dec 11, 2008 at 3:03 PM, Austin Nichols <austinnichols@gmail.com> wrote: > Richard Valliant <rvalliant@survey.umd.edu>: > Maybe you want: > rcal (e1=) (w: e2), suuinit(D) > (i.e. leave out only the x not the =x)? > See also http://www.stata-journal.com/sjpdf.html?articlenum=st0050 > > On Thu, Dec 11, 2008 at 11:07 AM, 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? > * * 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**:**Re: st: rcal, measurement error model question***From:*"Austin Nichols" <austinnichols@gmail.com>

**References**:**st: rcal, measurement error model question***From:*Richard Valliant <rvalliant@survey.umd.edu>

**Re: st: rcal, measurement error model question***From:*"Austin Nichols" <austinnichols@gmail.com>

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