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

Lucrezia Reichlin, Catherine Doz and Domenico Giannone

No 6043, CEPR Discussion Papers from C.E.P.R. Discussion Papers

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 (search for similar items in EconPapers)
JEL-codes: C32 C33 C51 (search for similar items in EconPapers)
Date: 2007-01
New Economics Papers: this item is included in nep-ecm and nep-ets
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Citations: View citations in EconPapers (47)

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Related works:
Journal Article: A two-step estimator for large approximate dynamic factor models based on Kalman filtering (2011)
Working Paper: A two-step estimator for large approximate dynamic factor models based on Kalman filtering (2011)
Working Paper: A two-step estimator for large approximate dynamic factor models based on Kalman filtering (2011)
Working Paper: A two-step estimator for large approximate dynamic factor models based on Kalman filtering (2011)
Working Paper: A two-step estimator for large approximate dynamic factor models based on Kalman filtering (2011)
Working Paper: A Two-step estimator for large approximate dynamic factor models based on Kalman filtering (2006)
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