Nonparametric bootstrap procedures for predictive inference based on recursive estimation schemes

Valentina Corradi, Norman R. Swanson

Research output: Contribution to journalArticlepeer-review

52 Scopus citations

Abstract

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 bootstrap due to Kilian (Journal of Applied 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.

Original languageAmerican English
Pages (from-to)67-109
Number of pages43
JournalInternational Economic Review
Volume48
Issue number1
DOIs
StatePublished - Feb 2007

ASJC Scopus subject areas

  • Economics and Econometrics

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