Forecasting nonlinear time series with neural network sieve bootstrap

Authors:
Francesco Giordano;Michele La Rocca;Cira Perna
Affiliations:
Department of Economics and Statistics, University of Salerno, via Ponte Don Melillo, 84084 Fisciano, Salerno, Italy;Department of Economics and Statistics, University of Salerno, via Ponte Don Melillo, 84084 Fisciano, Salerno, Italy;Department of Economics and Statistics, University of Salerno, via Ponte Don Melillo, 84084 Fisciano, Salerno, Italy
Venue:
Computational Statistics & Data Analysis
Year:
2007

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Abstract

A new method to construct nonparametric prediction intervals for nonlinear time series data is proposed. Within the framework of the recently developed sieve bootstrap, the new approach employs neural network models to approximate the original nonlinear process. The method is flexible and easy to implement as a standard residual bootstrap scheme while retaining the advantage of being a nonparametric technique. It is model-free within a general class of nonlinear processes and avoids the specification of a finite dimensional model for the data generating process. The results of a Monte Carlo study are reported in order to investigate the finite sample performances of the proposed procedure.