Bootstrap Inference in Partially Identified Models Defined by Moment Inequalities: Coverage of the Identified Set.
This paper introduces a novel bootstrap procedure to perform inference in a wide class of partially identified econometric models. We consider econometric models defined by finitely many weak moment inequalities,2 which encompass many applications of economic interest. The objective of our inferenti...
| Publicado en: | Econometrica Vol. 78; no. 2; pp. 735 - 754 |
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| Formato: | Artículo |
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Wiley-Blackwell
March 2010
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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=511483236&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 511483236 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00129682 ECN jtl: Econometrica issn: 00129682 maglogo: N pubinfo: dt: March 2010 vid: 78 iid: 2 pid: 480 pub: Wiley-Blackwell artinfo: ui: 511483236 10.3982/ECTA8056 ppf: 735 ppct: 19 formats: tig: atl: Bootstrap Inference in Partially Identified Models Defined by Moment Inequalities: Coverage of the Identified Set. aug: au: Bugni, Federico A. su: Probability theory Statistical bootstrapping Identification (Statistics) sug: subj: Probability theory Statistical bootstrapping Identification (Statistics) ab: This paper introduces a novel bootstrap procedure to perform inference in a wide class of partially identified econometric models. We consider econometric models defined by finitely many weak moment inequalities,2 which encompass many applications of economic interest. The objective of our inferential procedure is to cover the identified set with a prespecified probability.3 We compare our bootstrap procedure, a competing asymptotic approximation, and subsampling procedures in terms of the rate at which they achieve the desired coverage level, also known as the error in the coverage probability. Under certain conditions, we show that our bootstrap procedure and the asymptotic approximation have the same order of error in the coverage probability, which is smaller than that obtained by using subsampling. This implies that inference based on our bootstrap and asymptotic approximation should eventually be more precise than inference based on subsampling. A Monte Carlo study confirms this finding in a small sample simulation. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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