Information, data dimension and factor structure

Jan P. A. M. Jacobs*, Pieter W. Otter, Ard H. J. den Reijer

*Corresponding author for this work

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Abstract

This paper employs concepts from information theory for choosing the dimension of a data set. We propose a relative information measure connected to Kullback-Leibler numbers. By ordering the series of the data set according to the measure, we are able to obtain a subset of a data set that is most informative. The method can be used as a first step in the construction of a dynamic factor model or a leading index, as illustrated with a Monte Carlo study and with the US macroeconomic data set of Stock and Watson [20]. (C) 2011 Elsevier Inc. All rights reserved.

Original languageEnglish
Pages (from-to)80-91
Number of pages12
JournalJournal of Multivariate Analysis
Volume106
DOIs
Publication statusPublished - Apr-2012

Keywords

  • Kullback-Leibler numbers
  • Information
  • Factor structure
  • Data set dimension
  • Dynamic factor models
  • Leading index
  • DYNAMIC-FACTOR MODEL
  • NUMBER
  • PREDICTORS
  • SELECTION

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