Neural Network Sieve Bootstrap Prediction Intervals: Some Real Data Evidence

Authors:
Francesco Giordano;Michele La Rocca;Cira Perna
Affiliations:
Department of Economics and Statistics-University of Salerno, Via Ponte Don Melillo, 84084, Fisciano (SA)-Italy;Department of Economics and Statistics-University of Salerno, Via Ponte Don Melillo, 84084, Fisciano (SA)-Italy;Department of Economics and Statistics-University of Salerno, Via Ponte Don Melillo, 84084, Fisciano (SA)-Italy
Venue:
Proceedings of the 2009 conference on New Directions in Neural Networks: 18th Italian Workshop on Neural Networks: WIRN 2008
Year:
2009

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Abstract

A sieve bootstrap scheme, the neural network sieve bootstrap, for nonlinear time series is discussed. The approach, which is non parametric in its spirit, does not have the problems of other nonparametric bootstrap techniques. The procedure is used to construct prediction intervals and it takes into account the uncertainty associated with the estimation of the model parameters. An application to real data sets is also presented.