Open Access
May 2009 Toward optimal multistep forecasts in non-stationary autoregressions
Ching-Kang Ing, Jin-Lung Lin, Shu-Hui Yu
Bernoulli 15(2): 402-437 (May 2009). DOI: 10.3150/08-BEJ165

Abstract

This paper investigates multistep prediction errors for non-stationary autoregressive processes with both model order and true parameters unknown. We give asymptotic expressions for the multistep mean squared prediction errors and accumulated prediction errors of two important methods, plug-in and direct prediction. These expressions not only characterize how the prediction errors are influenced by the model orders, prediction methods, values of parameters and unit roots, but also inspire us to construct some new predictor selection criteria that can ultimately choose the best combination of the model order and prediction method with probability 1. Finally, simulation analysis confirms the satisfactory finite sample performance of the newly proposed criteria.

Citation

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Ching-Kang Ing. Jin-Lung Lin. Shu-Hui Yu. "Toward optimal multistep forecasts in non-stationary autoregressions." Bernoulli 15 (2) 402 - 437, May 2009. https://doi.org/10.3150/08-BEJ165

Information

Published: May 2009
First available in Project Euclid: 4 May 2009

zbMATH: 1200.62114
MathSciNet: MR2543868
Digital Object Identifier: 10.3150/08-BEJ165

Keywords: Accumulated prediction error , direct prediction , mean squared prediction error , Model selection , plug-in method

Rights: Copyright © 2009 Bernoulli Society for Mathematical Statistics and Probability

Vol.15 • No. 2 • May 2009
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