A Parametric Estimation Method for Dynamic Factor Models of Large Dimensions

Massimiliano Marcellino and George Kapetanios

No 5620, CEPR Discussion Papers from C.E.P.R. Discussion Papers

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 parametric methodology for estimating factors from large datasets based on state space models and discuss its theoretical properties. In particular, we show that it is possible to estimate consistently the factor space. We also develop a consistent information criterion for the determination of the number of factors to be included in the model. Finally, we conduct a set of simulation experiments that show that our approach compares well with existing alternatives.

Keywords: Factor models; Principal components; Subspace algorithms (search for similar items in EconPapers)
JEL-codes: C32 C51 E52 (search for similar items in EconPapers)
Date: 2006-04
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-mac
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Citations: View citations in EconPapers (45)

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Journal Article: A parametric estimation method for dynamic factor models of large dimensions (2009)
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