Home > Measurement Error > Measurement Error Models With Auxiliary Data

Measurement Error Models With Auxiliary Data

Letg(x, β) ≡ E[m(X∗p, β) | Xp= x] =Zm(x∗, β) fX∗p|Xp=x(x∗)dx∗then (2) implies, by the law of iterated expectation, that uniquely at β = βoEp[g(X, βo)] =Zg(x, βo) fXp(x)dx = 0. Access supplemental materials and multimedia. On the other hand, Horowitzand Manski (1995) used a different model of measurement error, where they assume that theobserved sample is contaminated or corrupted (for more on this issue, see Molinari, Thus under Assumption 5(2) (γ > d/2),Z10qlog N[](δ, 3γc(X , ω1), k · k2, p)dδ < ∞, 362 REVIEW OF ECONOMIC STUDIESand the class {eg(•, βo) : eg(•, βo) ∈ 3γc(X check over here

Read your article online and download the PDF from your email or your MyJSTOR account. In rare instances, a publisher has elected to have a "zero" moving wall, so their current issues are available in JSTOR shortly after publication. kProof (Theorem 2). For example, the auxiliary data weuse in the empirical illustration section come from validating the social security income for asubset of the primary sample respondents who reported their social security numbers.We https://www.jstor.org/stable/3700655

This and the fact that ˆg(x, βo) ∈ 3γc(X , ω1),γ > d/2 with probability approaching one as nv→ ∞ imply thatsupeg(•,βo)∈3γc(X ,ω1):keg(•,βo)−g(•,βo)k2, p=o(1)Z[eg(x, βo) − g(x, βo)]d[bFXp(x) − FXp(x)]= op Please sign in with your personal username and password or Register to obtain a username name and password for free. If serial correlations,heteroscedasticity, cluster structure or panel data structure are present in either or both data-sets,we will need to take into account these correlation structures in estimating the limiting varianceof Theorem

Section 3 provides the assumptions and the large sample distributional results.It also discusses the efficiency gain of combining the validation data and the primary data,and provides a simple consistent estimator of Learn more about a JSTOR subscription Have access through a MyJSTOR account? It is a trusted and definitive source on panel data, having been edited by Professor Badi Baltagi-widely recognized as one of the foremost econometricians in the area of panel data econometrics. Carroll and Wand (1991), Sepanski and Carroll (1993) and Lee andSepanski (1995).

An issue that arises is that individuals that providedtheir social security number might be a selected group. Contact your library if you do not have a username and password. Applied researchers are often interested in using the CPS data-setto implement a standard Mincer regression to compute returns to education and experience.However, income in the CPS data-set is mismeasured. Our main assumption requires that the conditional distribution of the true variables given the mismeasured variables is the same in the primary and auxiliary data.

The system returned: (22) Invalid argument The remote host or network may be down. This assumption is satisfied if the auxiliary data-set is a validatedstratified subsample of the primary data-set, or if it is a validation data-set that is drawn fromthe population. World CongressEconometric Society Monographs, ISSN 2059-2507Volume 51 of Econometric Society monographs: Econometric SocietyAuthorsEconometric Society. Need to Activate?

In Section 3we show that efficiency gains can be obtained by optimally combining moment conditions (2)and (3).2.2. errorEducation 0·0551 0·0691 0·055(4·84e−03) (3·02e−03) (5·9e−03)Experience −0·0156 0·0507 −0·019(0·0178) (0·011) (0·025)Experience23·8e−04 −7·89e−04 4·27e−04(3·78e−04) (2·26e−04) (4·76e−04)Race 0·17 0·136 0·18(0·0299) (0·023) (0·0368)Constant 8·75 8·18 8·81(0·153) (0·16) (0·321)and our estimator, while the estimator using The returns to schoolingusing LAD (second column) on the primary data is higher (at almost 7%). We illustrate our methods by estimating a returns to schooling censored quantile regression using the CPS/SSR 1978 exact match files where the dependent variable is measured with error of arbitrary kind.

This data-set has been used by Bound and Krueger (1991) and Bollinger(1998) to study the extent of measurement error in earnings. check my blog Articles by Tamer, E. This relationshipis then used with the primary data to estimate the parameters of interest. We will call the latter case “validate insample” case, which is relevant if the researcher collects the primary data first, and then decidesto validate a subset of the primary data based

You may access this article for 1 day for US$40.00. In Appendix A we show that under Assumptions3(1) and 3(5), kˆg − gk∞,ω= op(1). Purchase Short-Term Access Pay per View - If you would like to purchase short-term access you must have a personal account. this content For example, if the current year is 2008 and a journal has a 5 year moving wall, articles from the year 2002 are available.

For a quantile approach to studying female wages that also handles selection, see Buchinsky (1998).5. fX∗v|Xv=x= fX∗p|Xp=xfor all x in the support of Xpin Rd.This assumption implies that for each fixed β,g(x, β) = E[m(X∗v, β) | Xv= x] =Zm(x∗, β) fX∗v|Xv=x(x∗)dx∗hence information about g(x, β) Login Compare your access options × Close Overlay Preview not available Abstract We study the problem of parameter inference in (possibly non-linear and non-smooth) econometric models when the data are measured

You may access this article for 1 day for US$40.00.

Measurement Error Models with Auxiliary Data Xiaohong Chen, Han Hong and Elie Tamer The Review of Economic Studies Vol. 72, No. 2 (Apr., 2005), pp. 343-366 Published by: Oxford University Press Thenumber of observations in the primary sample is np= 7362 and in the validation sample isnv= 4809.We provide further evidence against the classical measurement error model in Figure 1.There, we divide This Article Review of Economic Studies (2005) 72 (2): 343-366. Unlimited access to purchased articles.

If you would like to access this item you must have a personal account. In both cases,  is given in (9), and can be consistently estimated byb =1nvXnvj=1(cυ∗vjbUvj)(cυ∗vjbUvj)0+nvn2pXnpi=1( ˆg(Xpi,ˆβ) ˆg(Xpi,ˆβ)0)bUvj= m(X∗vj,ˆβ) − ˆg(Xvj,ˆβ)cυ∗vj=1npXnpi=1pknv(Xpi)0P0vPvnv−1pknv(Xvj). (10)The idea behind our estimator (10) for v∗vj≡ fXp(Xvj)/ fXv(Xvj) is Read as much as you want on JSTOR and download up to 120 PDFs a year. http://threadspodcast.com/measurement-error/measurement-error-models-fuller-pdf.html For any1×d vector a = (a1, . . . , ad) of non-negative integers, we write |a| =Pdk=1ak, and for any x = (x1, . . . , xd)0∈ X ⊆

Note: In calculating the moving wall, the current year is not counted. We also provide simpleconsistent estimators of the asymptotic variance ofˆβ.3.1. Contact your library for more details. Please sign in below with your personal username and password or Register to obtain a username and password for free.

Then by definition,0 ≤ Ln( ˆg) − Ln( ˆg ± εn52nυ∗)= µn(`(Zj, ˆg) − `(Zj, ˆg ± εn52nυ∗)) + Ev(`(Zj, ˆg) − `(Zj, ˆg ± εn52nυ∗)).Simple calculation yieldsEv(`(Zj, ˆg) − `(Zj, The estimatorsIn this section, we provide estimators that can be used, under Assumption 1, to consistentlyestimate the parameter βo.2.2.1.