Information, data dimension and factor structure

Jan Jacobs, Pieter W. Otter and Ard Reijer ()

Journal of Multivariate Analysis, 2012, vol. 106, issue C, 80-91

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].

Keywords: Kullback–Leibler numbers; Information; Factor structure; Data set dimension; Dynamic factor models; Leading index (search for similar items in EconPapers)
JEL-codes: C32 C52 C82 (search for similar items in EconPapers)
Date: 2012
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Working Paper: Information, data dimension and factor structure (2011)
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DOI: 10.1016/j.jmva.2011.11.003

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