Essay of a Dynamic Regression by Principal Components Model for Correlated Time Series

Del Moral, M. J.; Valderrama, M. J.

doi:10.1007/978-3-662-01131-7_32

M. J. Del Moral³ &
M. J. Valderrama³

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Abstract

In this paper we analyze the use of dynamic regression by principal components models for correlated time series forecasting. The choice of an appropriate cutting point on input and output series allows us to study their principal component analysis and the selection of a forecasting model. Two basic issues are discussed on studies with simulated and real data: parsimony and principal components selection in the forecasting model.

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Authors and Affiliations

Department of Statistics and O. R., University of Granada, 18071, Granada, Spain
M. J. Del Moral & M. J. Valderrama

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M. J. Del Moral
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M. J. Valderrama
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Editors and Affiliations

Statistics Department, IACR-Rothamsted, AL5 2JQ, Harpenden, Herts, UK
Roger Payne
Department of Mathematics, University of Bristol, BS8 1TW, Bristol, UK
Peter Green

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Del Moral, M.J., Valderrama, M.J. (1998). Essay of a Dynamic Regression by Principal Components Model for Correlated Time Series. In: Payne, R., Green, P. (eds) COMPSTAT. Physica, Heidelberg. https://doi.org/10.1007/978-3-662-01131-7_32

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DOI: https://doi.org/10.1007/978-3-662-01131-7_32
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-1131-5
Online ISBN: 978-3-662-01131-7
eBook Packages: Springer Book Archive

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