A Parametric Estimation Method for Dynamic Factor Models of Large Dimensions

IGIER Working Paper No. 305

34 Pages Posted: 31 Mar 2006

See all articles by George Kapetanios

George Kapetanios

King's College, London

Massimiliano Giuseppe Marcellino

Bocconi University - Department of Economics; Centre for Economic Policy Research (CEPR)

Multiple version iconThere are 3 versions of this paper

Date Written: January 2006

Abstract

The estimation of dynamic factor models for large sets of variables has attracted considerable attention recently, due to the increased availability of large datasets. In this paper we propose a new parametric methodology for estimating factors from large datasets based on state space models and discuss its theoretical properties. In particular, we show that it is possible to estimate consistently the factor space. We also develop a consistent information criterion for the determination of the number of factors to be included in the model. Finally, we conduct a set of simulation experiments that show that our approach compares well with existing alternatives.

Keywords: Factor models, Principal components, Subspace algorithms

JEL Classification: C32, C51, E52

Suggested Citation

Kapetanios, George and Marcellino, Massimiliano, A Parametric Estimation Method for Dynamic Factor Models of Large Dimensions (January 2006). IGIER Working Paper No. 305, Available at SSRN: https://ssrn.com/abstract=893123 or http://dx.doi.org/10.2139/ssrn.893123

George Kapetanios

King's College, London ( email )

30 Aldwych
London, WC2B 4BG
United Kingdom
+44 20 78484951 (Phone)

Massimiliano Marcellino (Contact Author)

Bocconi University - Department of Economics ( email )

Via Gobbi 5
Milan, 20136
Italy

Centre for Economic Policy Research (CEPR) ( email )

London
United Kingdom

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