Information, data dimension and factor structure
AbstractThis 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 .
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Bibliographic InfoArticle provided by Elsevier in its journal Journal of Multivariate Analysis.
Volume (Year): 106 (2012)
Issue (Month): C ()
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Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description
Other versions of this item:
- Jan P.A.M. Jacobs & Pieter W. Otter & Ard H.J. den Reijer, 2011. "Information, data dimension and factor structure," CAMA Working Papers 2011-15, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
- Jan Jacobs & Pieter Otter & Ard den Reijer, 2007. "Information, data dimension and factor structure," DNB Working Papers 150, Netherlands Central Bank, Research Department.
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data
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