A Two-Step Estimator for Large Approximate Dynamic Factor Models Based on Kalman Filtering
University of Cergy-Pontoise - Department of Economics
Université Libre de Bruxelles (ULB) - European Center for Advanced Research in Economics and Statistics (ECARES); Centre for Economic Policy Research (CEPR)
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)
CEPR Discussion Paper No. 6043
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).
Number of Pages in PDF File: 45
Keywords: Factor Models, Kalman filter, large cross-sections, principal components
JEL Classification: C32, C33, C51working papers series
Date posted: June 28, 2007
© 2013 Social Science Electronic Publishing, Inc. All Rights Reserved.
This page was processed by apollo7 in 0.610 seconds