Inference in panel data models under attrition caused by unobservables.
This paper concerns identification and estimation of a finite-dimensional parameter in a panel data-model under nonignorable sample attrition. Attrition can depend on second period variables which are unobserved for the attritors but an independent refreshment sample from the marginal distribution o...
| Publicado en: | Journal of Econometrics Vol. 144; no. 2; pp. 430 - 447 |
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| Formato: | Artículo |
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Elsevier Science
June 2008
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=511388241&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 511388241 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 03044076 ECM jtl: Journal of Econometrics issn: 03044076 maglogo: N pubinfo: dt: June 2008 vid: 144 iid: 2 pid: 1004 pub: Elsevier Science artinfo: ui: 511388241 10.1016/j.jeconom.2008.03.002 ppf: 430 ppct: 17 formats: tig: atl: Inference in panel data models under attrition caused by unobservables. aug: au: Bhattacharya, Debopam su: Panel analysis Probability theory Estimation theory sug: subj: Panel analysis Probability theory Estimation theory ab: This paper concerns identification and estimation of a finite-dimensional parameter in a panel data-model under nonignorable sample attrition. Attrition can depend on second period variables which are unobserved for the attritors but an independent refreshment sample from the marginal distribution of the second period values is available. This paper shows that under a quasi-separability assumption, the model implies a set of conditional moment restrictions where the moments contain the attrition function as an unknown parameter. This formulation leads to (i) a simple proof of identification under strictly weaker conditions than those in the existing literature and, more importantly, (ii) a sieve-based root-n consistent estimate of the finite-dimensional parameter of interest. These methods are applicable to both linear and nonlinear panel data models with endogenous attrition and analogous methods are applicable to situations of endogenously missing data in a single cross-section. The theory is illustrated with a simulation exercise, using Current Population Survey data where a panel structure is introduced by the rotation group feature of the sampling process. Copyright (c) 2008 Elsevier B.V. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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