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Using the bootstrap for improved ARIMA model identification

โœ Scribed by Dr. Amir D. Aczel; Dr. Norman H. Josephy

Publisher
John Wiley and Sons
Year
1992
Tongue
English
Weight
455 KB
Volume
11
Category
Article
ISSN
0277-6693

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โœฆ Synopsis

Abstract

This paper presents a new method of identifying ARIMA timeโ€series models. We use the bootstrap technique in estimating the distribution of sample autocorrelations both separately and in a simultaneous inference setting. The bootstrap has the advantage of being nonparametric and thus free of reliance on asymptotic normality, which may not hold for short or mediumโ€size series. The simultaneous procedure is unique, as it has no feasible parametric alternatives. An application to exchange rates illustrates our methodology. In the example chosen, we are able to produce better forecasts using the model identified via the bootstrap technique.

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