Group invariance, likelihood ratio tests, and the incidental parameter problem in a high-dimensional linear model

dc.contributor.authorHallin, Marc
dc.contributor.authorMoreira, Marcelo J.
dc.contributor.authorOnatski, Alexei
dc.date.accessioned2013-01-23T09:55:00Z
dc.date.available2013-01-23T09:55:00Z
dc.date.issued2013-01-23
dc.description.abstractThis paper considers a linear panel data model with reduced rank regressors and interactive fixed eff ects. The leading example is a factor model where some of the factors are observed, some others not. Invariance considerations yield a maximal invariant statistic whose density does not depend on incidental parameters. It is natural to consider a likelihood ratio test based on the maximal invariant statistic. Its density can be found by using as a prior the unique invariant distribution for the incidental parameters. That invariant distribution is least favorable and leads to minimax optimality properties. Combining the invariant distribution with a prior for the remaining parameters gives a class of admissible tests. A particular choice of distribution yields the spiked covariance model of Johnstone (2001). Numerical simulations suggest that the maximal invariant likelihood ratio test outperforms the standard likelihood ratio test. Tests which are not invariant to data transformations (i) are uniquely represented as randomized tests of the maximal invariant statistic and (ii) do not solve the incidental parameter problem.en
dc.identifier.urihttp://hdl.handle.net/2003/29872
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-4978
dc.language.isoende
dc.relation.ispartofseriesDiscussion Paper / SFB 823;01/2013
dc.subjectfactor modelsen
dc.subjectincidental parametersen
dc.subjectintegrated likelihooden
dc.subjectinvarianceen
dc.subjectlikelihood ratio testen
dc.subjectminimaxen
dc.subjectpanel data modelsen
dc.subject.ddc310
dc.subject.ddc330
dc.subject.ddc620
dc.titleGroup invariance, likelihood ratio tests, and the incidental parameter problem in a high-dimensional linear modelen
dc.typeTextde
dc.type.publicationtypeworkingPaperde
dcterms.accessRightsopen access

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