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dc.contributor.authorLawford, Stevede
dc.contributor.authorStamatogiannis, Michalis P.de
dc.date.accessioned2011-02-07T02:52:00Zde
dc.date.accessioned2012-08-29T23:11:09Z
dc.date.available2012-08-29T23:11:09Z
dc.date.issued2009de
dc.identifier.urihttp://www.ssoar.info/ssoar/handle/document/21575
dc.description.abstractVector autoregressions (VARs) are important tools in time series analysis. However, relatively little is known about the finite-sample behaviour of parameter estimators. We address this issue, by investigating ordinary least squares (OLS) estimators given a data generating process that is a purely nonstationary first-order VAR. Specifically, we use Monte Carlo simulation and numerical optimisation to derive response surfaces for OLS bias and variance, in terms of VAR dimensions, given correct specification and several types of over-parameterisation of the model: we include a constant, and a constant and trend, and introduce excess lags. We then examine the correction factors that are required for the least squares estimator to attain the minimum mean squared error (MSE). Our results improve and extend one of the main finite-sample multivariate analytical bias results of Abadir, Hadri and Tzavalis [Abadir, K.M., Hadri, K., Tzavalis, E., 1999. The influence of VAR dimensions on estimator biases. Econometrica 67, 163–181], generalise the univariate variance and MSE findings of Abadir [Abadir, K.M., 1995. Unbiased estimation as a solution to testing for random walks. Economics Letters 47, 263–268] to the multivariate setting, and complement various asymptotic studies.en
dc.languageende
dc.subject.ddcWirtschaftde
dc.subject.ddcEconomicsen
dc.subject.otherFinite-sample bias; Monte Carlo simulation; Nonstationary time series; Response surface; Vector autoregression; JEL classification: C15; C22; C32
dc.titleThe finite-sample effects of VAR dimensions on OLS bias, OLS variance, and minimum MSE estimatorsen
dc.description.reviewbegutachtet (peer reviewed)de
dc.description.reviewpeer revieweden
dc.source.journalJournal of Econometricsde
dc.source.volume148de
dc.publisher.countryNLD
dc.source.issue2de
dc.subject.classozEconomic Statistics, Econometrics, Business Informaticsen
dc.subject.classozWirtschaftsstatistik, Ökonometrie, Wirtschaftsinformatikde
dc.identifier.urnurn:nbn:de:0168-ssoar-215759de
dc.date.modified2011-02-07T16:09:00Zde
dc.rights.licencePEER Licence Agreement (applicable only to documents from PEER project)de
dc.rights.licencePEER Licence Agreement (applicable only to documents from PEER project)en
ssoar.gesis.collectionSOLIS;ADISde
ssoar.contributor.institutionhttp://www.peerproject.eu/de
internal.status3de
dc.type.stockarticlede
dc.type.documentjournal articleen
dc.type.documentZeitschriftenartikelde
dc.rights.copyrightfde
dc.source.pageinfo124-130
internal.identifier.classoz10905
internal.identifier.journal195de
internal.identifier.document32
internal.identifier.ddc330
dc.identifier.doihttps://doi.org/10.1016/j.jeconom.2008.10.004de
dc.description.pubstatusPostprinten
dc.description.pubstatusPostprintde
internal.identifier.licence7
internal.identifier.pubstatus2
internal.identifier.review1
internal.check.abstractlanguageharmonizerCERTAIN
internal.check.languageharmonizerCERTAIN_RETAINED


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