A Comparison of Estimation Methods for Dynamic Factor Models of Large Dimensions

U of London Queen Mary Economics Working Paper No. 489

53 Pages Posted: 12 May 2003

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)

Date Written: March 2003

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 methodology for estimating factors from large datasets based on state space models, discuss its theoretical properties and compare its performance with that of two alternative estimation approaches based, respectively, on static and dynamic principal components. The new method appears to perform best in recovering the factors in a set of simulation experiments, with static principal components a close second best. Dynamic principal components appear to yield the best fit, but sometimes there are leakages across the common and idiosyncratic components of the series. A similar pattern emerges in an empirical application with a large dataset of US macroeconomic time series.

JEL Classification: C32, C51, E52

Suggested Citation

Kapetanios, George and Marcellino, Massimiliano, A Comparison of Estimation Methods for Dynamic Factor Models of Large Dimensions (March 2003). U of London Queen Mary Economics Working Paper No. 489, Available at SSRN: https://ssrn.com/abstract=394340 or http://dx.doi.org/10.2139/ssrn.394340

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

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
286
Abstract Views
1,572
rank
131,398
PlumX Metrics