Nearly Optimal Tests When a Nuisance Parameter Is Present Under the Null Hypothesis.
This paper considers nonstandard hypothesis testing problems that involve a nuisance parameter. We establish an upper bound on the weighted average power of all valid tests, and develop a numerical algorithm that determines a feasible test with power close to the bound. The approach is illustrated i...
| Publicado en: | Econometrica Vol. 83; no. 2; pp. 771 - 812 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Wiley-Blackwell
Mar2015
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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=101868367&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 101868367 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00129682 ECN jtl: Econometrica issn: 00129682 maglogo: Y pubinfo: dt: Mar2015 vid: 83 iid: 2 pid: 480 pub: Wiley-Blackwell artinfo: ui: 101868367 10.3982/ECTA10535 ppf: 771 ppct: 41 formats: tig: atl: Nearly Optimal Tests When a Nuisance Parameter Is Present Under the Null Hypothesis. aug: au: Elliott, Graham Müller, Ulrich K. Watson, Mark W. affil: University of California, San Diego Dept. of Economics, Princeton University NBER su: Parameter estimation Boundary value problems Gaussian function Gaussian distribution Regression analysis sug: subj: Parameter estimation Boundary value problems Gaussian function Gaussian distribution Regression analysis keyword: composite hypothesis Least favorable distribution maximin tests composite hypothesis Least favorable distribution maximin tests ab: This paper considers nonstandard hypothesis testing problems that involve a nuisance parameter. We establish an upper bound on the weighted average power of all valid tests, and develop a numerical algorithm that determines a feasible test with power close to the bound. The approach is illustrated in six applications: inference about a linear regression coefficient when the sign of a control coefficient is known; small sample inference about the difference in means from two independent Gaussian samples from populations with potentially different variances; inference about the break date in structural break models with moderate break magnitude; predictability tests when the regressor is highly persistent; inference about an interval identified parameter; and inference about a linear regression coefficient when the necessity of a control is in doubt. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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