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
Date Written: June 1, 2011
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: Suggested Citation