Nonparametric Bootstrap Procedures for Predictive Inference Based on Recursive Estimation Schemes

Nonparametric Bootstrap Procedures for Predictive Inference Based on Recursive Estimation Schemes (2005)

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by Valentina Corradi , Norman R. Swanson

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BibTeX

@MISC{Corradi05nonparametricbootstrap,
    author = {Valentina Corradi and Norman R. Swanson},
    title = {Nonparametric Bootstrap Procedures for Predictive Inference Based on Recursive Estimation Schemes },
    year = {2005}
}

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Abstract

We introduce block bootstrap techniques that are (first order) valid in recursive estimation frameworks. There-after, we present two examples where predictive accuracy tests are made operational using our new bootstrap proce-dures. 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. In a Monte Carlo investigation, we compare the finite sample properties of our block bootstrap procedures with the parametric bootstrap due to Kilian (1999); 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.

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