Information, Data Dimension and Factor Structure

The Australian National University Centre for Applied Microeconomic Analysis Working Paper No. 15/2011

31 Pages Posted: 22 Jun 2011

See all articles by Jan P. A. M. Jacobs

Jan P. A. M. Jacobs

University of Groningen - Faculty of Economics and Business

Pieter Otter

University of Groningen

Ard den Reijer

Sveriges Riksbank - Monetary Policy

Multiple version iconThere are 2 versions of this paper

Date Written: June 1, 2011

Abstract

This paper employs concepts from information theory to 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 U.S. macroeconomic data set of Stock and Watson.

Keywords: Kullback-Leibler Numbers, Information, Factor Structure, Data Set Dimension, Dynamic Factor Models, Leading Index

JEL Classification: C32, C52, C82

Suggested Citation

Jacobs, Jan P.A.M. and Otter, Pieter and den Reijer, Ard, Information, Data Dimension and Factor Structure (June 1, 2011). The Australian National University Centre for Applied Microeconomic Analysis Working Paper No. 15/2011. Available at SSRN: https://ssrn.com/abstract=1868588 or http://dx.doi.org/10.2139/ssrn.1868588

Jan P.A.M. Jacobs (Contact Author)

University of Groningen - Faculty of Economics and Business ( email )

Postbus 72
9700 AB Groningen
Netherlands

Pieter Otter

University of Groningen ( email )

P.O. Box 800
9700 AH Groningen, Groningen 9700 AV
Netherlands

Ard den Reijer

Sveriges Riksbank - Monetary Policy ( email )

SE-103 37 Stockholm
Sweden

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