MMC techniques for limited dependent variables models: Implementation by the branch-and-bound algorithm.
We propose a finite sample approach to some of the most common limited dependent variables models. The method rests on the maximized Monte Carlo (MMC) test technique proposed by Dufour [1998. Monte Carlo tests with nuisance parameters: a general approach to finite-sample inference and nonstandard as...
| Publicado en: | Journal of Econometrics Vol. 133; no. 2; pp. 479 - 513 |
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| Autores principales: | , |
| Formato: | Artículo |
| Publicado: |
Elsevier Science
August 2006
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| Materias: | |
| 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=511308660&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 511308660 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: August 2006 vid: 133 iid: 2 pid: 1004 pub: Elsevier Science artinfo: ui: 511308660 10.1016/j.jeconom.2005.06.006 ppf: 479 ppct: 34 formats: tig: atl: MMC techniques for limited dependent variables models: Implementation by the branch-and-bound algorithm. aug: au: Jouneau-Sion, Frédéric Torrès, Olivier su: Monte Carlo method Algorithms Probability theory sug: subj: Monte Carlo method Algorithms Probability theory ab: We propose a finite sample approach to some of the most common limited dependent variables models. The method rests on the maximized Monte Carlo (MMC) test technique proposed by Dufour [1998. Monte Carlo tests with nuisance parameters: a general approach to finite-sample inference and nonstandard asymptotics. Journal of Econometrics, this issue]. We provide a general way for implementing tests and confidence regions. We show that the decision rule associated with a MMC test may be written as a Mixed Integer Programming problem. The branch-and-bound algorithm yields a global maximum in finite time. An appropriate choice of the statistic yields a consistent test, while fulfilling the level constraint for any sample size. The technique is illustrated with numerical data for the logit model. Copyright (c) 2006 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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