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Dimension Reduction in Multivariate Time Series

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Book cover Advances in Distribution Theory, Order Statistics, and Inference

Part of the book series: Statistics for Industry and Technology ((SIT))

Abstract

This chapter compares models for dimension reduction in time series and tests of the dimension of the dynamic structure. We consider both stationary and nonstationary time series and discuss principal components, canonical analysis, scalar component models, reduced rank models, and factor models. The unifying view of canonical correlation analysis between the present and past values of the series is emphasized. Then, we review some of the tests based on canonical correlation analysis to find the dimension of the dynamic relationship among the time series. Finally, the procedures are compared through a real data example.

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Author information

Authors and Affiliations

  1. Universidad Carlos III de Madrid, Madrid, Spain

    Daniel Peña & Pilar Poncela

  2. Universidad Autónoma de Madrid, Madrid, Spain

    Daniel Peña & Pilar Poncela

Authors
  1. Daniel Peña
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  2. Pilar Poncela
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Editor information

Editors and Affiliations

  1. Department of Mathematics and Statistics, McMaster University, 1280 Main Street West, Hamilton, Ontario, L8S 4K1, Canada

    N. Balakrishnan

  2. Department of Economics, University of Cantabria, s/n Avenida de los Castros, Santander, 39005, Spain

    José María Sarabia  & Enrique Castillo  & 

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© 2006 Birkhäuser Boston

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Peña, D., Poncela, P. (2006). Dimension Reduction in Multivariate Time Series. In: Balakrishnan, N., Sarabia, J.M., Castillo, E. (eds) Advances in Distribution Theory, Order Statistics, and Inference. Statistics for Industry and Technology. Birkhäuser Boston. https://doi.org/10.1007/0-8176-4487-3_28

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