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dc.contributor.authorHendry, David F.-
dc.contributor.authorKrolzig, Martin-
dc.date.accessioned2008-05-29T13:26:14Z-
dc.date.available2008-05-29T13:26:14Z-
dc.date.issued2004-10-08-
dc.identifier.urihttp://hdl.handle.net/2451/26938-
dc.description.abstractAfter reviewing the simulation performance of general-to-specific automatic regression model selection, as embodied in PcGets, we show how model selection can be non-distortionary: approximately unbiased ‘selection estimates’ are derived, with reported standard errors close to the sampling standard deviations of the estimated DGP parameters, and a near-unbiased goodness-of-fit measure. The handling of theory-based restrictions, non-stationarity, and problems posed by collinear data are considered. Finally, we consider how PcGets can handle three ‘intractable’ problems: more variables than observations in regression analysis; perfectly collinear regressors; and modelling simultaneous equations without a priori restrictions.en
dc.language.isoen_USen
dc.relation.ispartofseriesSC-CFE-05-03en
dc.subjectEconometric methodologyen
dc.subjectmodel selectionen
dc.subjectgeneral-to-specificen
dc.subjectautomaticen
dc.titleThe Properties of Automatic Gets Modellingen
dc.typeWorking Paperen
Appears in Collections:Financial Econometrics

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