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Medientyp:
E-Artikel
Titel:
Nonparametric Bootstrap Procedures for Predictive Inference Based on Recursive Estimation Schemes
Beteiligte:
Corradi, Valentina;
Swanson, Norman R.
Erschienen:
Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association, 2007
Erschienen in:
International Economic Review, 48 (2007) 1, Seite 67-109
Sprache:
Englisch
ISSN:
0020-6598;
1468-2354
Entstehung:
Anmerkungen:
Beschreibung:
<p>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 among multiple alternative forecasting models, all of which are possibly misspecified. In a Monte Carlo investigation, we compare the finite sample properties of our block bootstrap procedures with the parametric boot due to Kilian (Journal ofApplied Econometrics 14 (1999), 491-510), within the context of encompassing and predictive accuracy tests. In the empirical illustration, it is found that unemployment has nonlinear marginal predictive content for inflation.</p>