Identification and Estimation of Causal Factor Models of Stationary Time Series

Chris Heaton and Victor Solo
Additional contact information
Victor Solo: School of Electrical Engineering and Telecommunications, University of New South Wales

No 201, Research Papers from Macquarie University, Department of Economics

Abstract: This paper makes three contributions to the literature on dynamic factor analysis. Firstly, we investigate the identification problem for a general class of causal dynamic factor model and provide conditions under which the model is identified. Secondly, we present an analytical expression for the information matrix of an autoregressive factor model which can be computed far more efficiently than the standard numerical expression, and thirdly we propose an accelerated EM algorithm which has the same convergence properties as the traditional scoring algorithm but has the same storage and CPU-time requirements per iteration as the standard EM algorithm. We illustrate the very significant computational gains over the standard approach with simulations.

Keywords: Dynamic factor analysis; time series; state-space models (search for similar items in EconPapers)
Pages: 26 pages.
Date: 2002-03
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.econ.mq.edu.au/research/2002/1-2002HeatonV2.pdf First Version, 2002 (application/pdf)
Our link check indicates that this URL is bad, the error code is: 500 Can't connect to www.econ.mq.edu.au:80 (No such host is known. )

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:mac:wpaper:0201

Access Statistics for this paper

More papers in Research Papers from Macquarie University, Department of Economics Contact information at EDIRC.
Bibliographic data for series maintained by Helen Boneham ( this e-mail address is bad, please contact ).