Impossibility Results for Nondifferentiable Functionals.
We examine challenges to estimation and inference when the objects of interest are nondifferentiable functionals of the underlying data distribution. This situation arises in a number of applications of bounds analysis and moment inequality models, and in recent work on estimating optimal dynamic tr...
| Publicado en: | Econometrica Vol. 80; no. 4; pp. 1769 - 1791 |
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| Autores principales: | , |
| Formato: | Artículo |
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Wiley-Blackwell
Jul2012
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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=78110085&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 78110085 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00129682 ECN jtl: Econometrica issn: 00129682 maglogo: Y pubinfo: dt: Jul2012 vid: 80 iid: 4 pid: 480 pub: Wiley-Blackwell artinfo: ui: 78110085 10.3982/ECTA8681 ppf: 1769 ppct: 22 formats: tig: atl: Impossibility Results for Nondifferentiable Functionals. aug: au: Hirano, Keisuke Porter, Jack R. affil: Dept. of Economics, University of Arizona, Tucson, AZ 85721, U.S.A.; Dept. of Economics, University of Wisconsin, Madison, WI 53706, U.S.A.; su: Nondifferentiable functions Mathematical statistics Data distribution Mathematical bounds Asymptotic distribution sug: subj: Nondifferentiable functions Mathematical statistics Data distribution Mathematical bounds Asymptotic distribution keyword: bias-correction bounds Local asymptotics moment inequality models bias-correction bounds Local asymptotics moment inequality models ab: We examine challenges to estimation and inference when the objects of interest are nondifferentiable functionals of the underlying data distribution. This situation arises in a number of applications of bounds analysis and moment inequality models, and in recent work on estimating optimal dynamic treatment regimes. Drawing on earlier work relating differentiability to the existence of unbiased and regular estimators, we show that if the target object is not differentiable in the parameters of the data distribution, there exist no estimator sequences that are locally asymptotically unbiased or α-quantile unbiased. This places strong limits on estimators, bias correction methods, and inference procedures, and provides motivation for considering other criteria for evaluating estimators and inference procedures, such as local asymptotic minimaxity and one-sided quantile unbiasedness. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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