A Two-Step Estimator for Large Approximate Dynamic Factor Models Based on Kalman Filtering

45 Pages Posted: 28 Jun 2007

See all articles by Catherine Doz

Catherine Doz

University of Cergy-Pontoise - Department of Economics

Domenico Giannone

Federal Reserve Banks - Federal Reserve Bank of New York; Centre for Economic Policy Research (CEPR)

Lucrezia Reichlin

London Business School; Université Libre de Bruxelles (ULB) - European Center for Advanced Research in Economics and Statistics (ECARES); Centre for Economic Policy Research (CEPR); European Central Bank (ECB)

Date Written: January 2007

Abstract

This paper shows consistency of a two step estimator of the parameters of a dynamic approximate factor model when the panel of time series is large (n large). In the first step, the parameters are first estimated from an OLS on principal components. In the second step, the factors are estimated via the Kalman smoother. This projection allows to consider dynamics in the factors and heteroskedasticity in the idiosyncratic variance. The analysis provides theoretical backing for the estimator considered in Giannone, Reichlin, and Sala (2004) and Giannone, Reichlin, and Small (2005).

Keywords: Factor Models, Kalman filter, large cross-sections, principal components

JEL Classification: C32, C33, C51

Suggested Citation

Doz, Catherine and Giannone, Domenico and Reichlin, Lucrezia, A Two-Step Estimator for Large Approximate Dynamic Factor Models Based on Kalman Filtering (January 2007). CEPR Discussion Paper No. 6043. Available at SSRN: https://ssrn.com/abstract=997018

Catherine Doz (Contact Author)

University of Cergy-Pontoise - Department of Economics ( email )

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Domenico Giannone

Federal Reserve Banks - Federal Reserve Bank of New York ( email )

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Lucrezia Reichlin

London Business School ( email )

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