MAXIMUM LIKELIHOOD ESTIMATION OF FACTOR MODELS ON DATASETS WITH ARBITRARY PATTERN OF MISSING DATA
Marta Banbura () and
Michele Modugno ()
Journal of Applied Econometrics, 2014, vol. 29, issue 1, 133-160
SUMMARY In this paper we modify the expectation maximization algorithm in order to estimate the parameters of the dynamic factor model on a dataset with an arbitrary pattern of missing data. We also extend the model to the case with a serially correlated idiosyncratic component. The framework allows us to handle efficiently and in an automatic manner sets of indicators characterized by different publication delays, frequencies and sample lengths. This can be relevant, for example, for young economies for which many indicators have been compiled only recently. We evaluate the methodology in a Monte Carlo experiment and we apply it to nowcasting of the euro area gross domestic product. Copyright © 2012 John Wiley & Sons, Ltd.
References: Add references at CitEc
Citations: View citations in EconPapers (89) Track citations by RSS feed
Downloads: (external link)
Working Paper: Maximum likelihood estimation of factor models on data sets with arbitrary pattern of missing data (2010)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:wly:japmet:v:29:y:2014:i:1:p:133-160
Ordering information: This journal article can be ordered from
http://www3.intersci ... e.jsp?issn=0883-7252
Access Statistics for this article
Journal of Applied Econometrics is currently edited by M. Hashem Pesaran
More articles in Journal of Applied Econometrics from John Wiley & Sons, Ltd.
Bibliographic data for series maintained by Wiley Content Delivery ().