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dc.contributor.authorCorradi, Valentinaen_US
dc.contributor.authorSwanson, Norman R.en_US
dc.date.accessioned2008-05-16en_US
dc.date.accessioned2010-05-14T11:02:17Z-
dc.date.available2010-05-14T11:02:17Z-
dc.date.issued2005en_US
dc.identifier.urihttp://hdl.handle.net/10419/31299-
dc.description.abstractOur objectives in this paper are twofold. First, we introduce block bootstrap techniques that are (first order) valid in recursive estimation frameworks. Thereafter, we present two examples where predictive accuracy tests are made operational using our new bootstrap procedures. In one application, we outline a consistent test for out-of-sample nonlinear Granger causality, and in the other we outline a test for selecting amongst multiple alternative forecasting models, all of which are possibly misspecified. More specifically, our examples extend the White (2000) reality check to the case of non vanishing parameter estimation error, and extend the integrated conditional moment tests of Bierens (1982, 1990) and Bierens and Ploberger (1997) to the case of out-of-sample prediction. In both examples, appropriate re-centering of the bootstrap score is required in order to ensure that the tests have asymptotically correct size, and the need for such re-centering is shown to arise quite naturally when testing hypotheses of predictive accuracy. In a Monte Carlo investigation, we compare the finite sample properties of our block bootstrap procedures with the parametric bootstrap due to Kilian (1999); all within the context of various encompassing and predictive accuracy tests. An empirical illustration is also discussed, in which it is found that unemployment appears to have nonlinear marginal predictive content for inflation.en_US
dc.language.isoengen_US
dc.publisherDep. of Economics, Rutgers, the State Univ. of New Jersey New Brunswick, NJen_US
dc.relation.ispartofseriesWorking papers // Department of Economics, Rutgers, the State University of New Jersey 2006,18en_US
dc.subject.jelC22en_US
dc.subject.jelC51en_US
dc.subject.ddc330en_US
dc.subject.keywordBlock bootstrapen_US
dc.subject.keywordnonlinear causalityen_US
dc.subject.keywordparameter estimation erroren_US
dc.subject.keywordreality checken_US
dc.subject.keywordrecursive estimation schemeen_US
dc.titleNonparametric bootstrap procedures for predictive inference based on recursive estimation schemesen_US
dc.typeWorking Paperen_US
dc.identifier.ppn566316137en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungen-
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