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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 11:41:03 -0500 |

Richard Valliant <rvalliant@survey.umd.edu>: Below code corrects the aweight typo, and makes better fake variances, and when I wrote "I am making no use of the individual-observation-specific error variances you have" I meant "making no use of the individual-observation-specific error variances for e2" not e1, since v1 is incorporated in the aweight. set seed 1 drawnorm e1 v1 e2 v2, n(30) clear replace v1=exp(v1-2) replace v2=exp(v2-2) su e2 loc e2var=r(Var) su v2 loc v2var=r(mean) loc r=(`e2var'-`v2var')/`e2var' eivreg e1 e2 [aw=1/v1], r(e2 `r') mat r=(`v2var') rcal (e1=) (w: e2), suuinit(r) What's the ultimate goal of this exercise? Is e2 actually supposed to have a linear effect on e1? Maybe a simulation would help... cap prog drop evrcal prog evrcal, rclass drawnorm z v1 e2 v2, n(30) clear replace v1=exp(v1-3) replace v2=exp(v2-3) g err1=invnormal(uniform())*v1 g err2=invnormal(uniform())*v2 g o2=e2+err2 g e1=1+(o2)/2+z g o1=e1+err1 su o2 loc e2var=r(Var) su v2 loc v2var=r(mean) loc r=(`e2var'-`v2var')/`e2var' eivreg o1 o2 [aw=1/v1], r(o2 `r') return scalar be=_b[o2] return scalar se=_se[o2] test o2=.5 return scalar pe=r(p) mat r=(`v2var') rcal (o1=) (w: o2), suuinit(r) return scalar br=_b[w] return scalar sr=_se[w] test _b[w]=.5 return scalar pr=r(p) eret clear end simul, reps(10000): evrcal g rejr=pr<.05 g reje=pe<.05 su, sep(4) On Fri, Dec 12, 2008 at 10:18 AM, Austin Nichols <austinnichols@gmail.com> wrote: > 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/

**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>

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

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