Likelihood‐based dynamic factor analysis for measurement and forecasting

Borus Jungbacker and Siem Jan Koopman

Econometrics Journal, 2015, vol. 18, issue 2, C1-C21

Abstract: We present new results for the likelihood‐based analysis of the dynamic factor model. The latent factors are modelled by linear dynamic stochastic processes. The idiosyncratic disturbance series are specified as autoregressive processes with mutually correlated innovations. The new results lead to computationally efficient procedures for the estimation of the factors and for the parameter estimation by maximum likelihood methods. We also present the implications of our results for models with regression effects, for Bayesian analysis, for signal extraction, and for forecasting. An empirical illustration is provided for the analysis of a large panel of macroeconomic time series.

Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (34)

Downloads: (external link)
http://hdl.handle.net/10.1111/ectj.12029

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:wly:emjrnl:v:18:y:2015:i:2:p:c1-c21

Ordering information: This journal article can be ordered from
http://onlinelibrary ... 1111/(ISSN)1368-423X

Access Statistics for this article

Econometrics Journal is currently edited by Jaap Abbring, Victor Chernozhukov, Michael Jansson and Dennis Kristensen

More articles in Econometrics Journal from Royal Economic Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().