Dynamic Factor Analysis in The Presence of Missing Data

B. Jungbacker (), Siem Jan Koopman () and Michel van der Wel ()
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B. Jungbacker: VU University Amsterdam

No 09-010/4, Tinbergen Institute Discussion Papers from Tinbergen Institute

Abstract: We develop a new model representation for high-dimensional dynamic multi-factor models. It allows the Kalman filter and related smoothing methods to produce optimal estimates in a computationally efficient way in the presence of missing data. We discuss the model in detail together with the implementation of methods for signal extraction and parameter estimation. The computational gains of the new devices are presented based on simulated data-sets with varying numbers of missing entries.

Keywords: High-dimensional vector series; Kalman Filter; Maximum likelihood (search for similar items in EconPapers)
JEL-codes: C33 C43 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-ets
Date: 2009-02-12
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Persistent link: http://EconPapers.repec.org/RePEc:dgr:uvatin:20090010

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