A Two-Step Estimator for Large Approximate Dynamic Factor Models Based on Kalman Filtering
45 Pages Posted: 28 Jun 2007
Date Written: January 2007
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: Suggested Citation