Bootstrap inference in econometrics.
The astonishing increase in computer performance over the past two decades has made it possible for economists to base many statistical inferences on simulated, or bootstrap, distributions rather than on distributions obtained from asymptotic theory. In this paper, I review some of the basic ideas o...
| Publicado en: | Canadian Journal of Economics Vol. 35; no. 4; pp. 615 - 646 |
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| Formato: | Artículo |
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Wiley-Blackwell
November 2002
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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=513157578&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 513157578 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00084085 CJE jtl: Canadian Journal of Economics issn: 00084085 maglogo: N pubinfo: dt: November 2002 vid: 35 iid: 4 pid: 480 pub: Wiley-Blackwell artinfo: ui: 513157578 10.1111/0008-4085.00147 ppf: 615 ppct: 31 formats: tig: atl: Bootstrap inference in econometrics. aug: au: MacKinnon, James G. su: Econometrics Statistical bootstrapping Probability theory sug: subj: Econometrics Statistical bootstrapping Probability theory ab: The astonishing increase in computer performance over the past two decades has made it possible for economists to base many statistical inferences on simulated, or bootstrap, distributions rather than on distributions obtained from asymptotic theory. In this paper, I review some of the basic ideas of bootstrap inference. I discuss Monte Carlo tests, several types of bootstrap test, and bootstrap confidence intervals. Although bootstrapping often works well, it does not do so in every case. Reprinted by permission of the publisher. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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