Accurate and (almost) tuning parameter free inference in cointegrating regressions

Reichold, Karsten; Jentsch, Carsten

Accurate and (almost) tuning parameter free inference in cointegrating regressions

dc.contributor.author	Reichold, Karsten
dc.contributor.author	Jentsch, Carsten
dc.date.accessioned	2021-01-04T07:47:00Z
dc.date.available	2021-01-04T07:47:00Z
dc.date.issued	2020
dc.description.abstract	Tuning parameter choices complicate statistical inference in cointegrating regressions and affect finite sample distributions of test statistics. As commonly used asymptotic theory fails to capture these effects, tests often suffer from severe size distortions. We propose a novel self-normalized test statistic for general linear hypotheses, which avoids the choice of tuning parameters. Its limiting null distributions is nonstandard, but simulating asymptotically valid critical values is straightforward. To further improve the performance of the test in small to medium samples, we employ the vector autoregressive sieve bootstrap to construct critical values. To show its consistency, we establish a bootstrap invariance principle result under conditions that go beyond the assumptions commonly imposed in the literature. Simulation results demonstrate that our new test outperforms competing approaches, as it has good power properties and is considerably less prone to size distortions.	de
dc.identifier.uri	http://hdl.handle.net/2003/39964
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-21854
dc.language.iso	en	de
dc.relation.ispartofseries	Discussion Paper / SFB823;33/2020
dc.subject	bootstrap invariance principle	en
dc.subject	vector autoregressive sieve bootstrap	en
dc.subject	self-normalization	en
dc.subject	inference	en
dc.subject	IM-OLS	en
dc.subject	cointegration	en
dc.subject.ddc	310
dc.subject.ddc	330
dc.subject.ddc	620
dc.subject.rswk	Bootstrap-Statistik	de
dc.subject.rswk	Inferenzstatistik	de
dc.subject.rswk	Kointegration	de
dc.title	Accurate and (almost) tuning parameter free inference in cointegrating regressions	en
dc.type	Text	de
dc.type.publicationtype	workingPaper	de
dcterms.accessRights	open access
eldorado.secondarypublication	false	de

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