MAXIMUM LIKELIHOOD ESTIMATION OF FACTOR MODELS ON DATASETS WITH ARBITRARY PATTERN OF MISSING DATA

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 h...

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Bibliographic Details
Published in:Journal of applied econometrics (Chichester, England) England), 2014-01, Vol.29 (1), p.133-160
Main Authors: Bańbura, Marta, Modugno, Michele
Format: Article
Language:English
Subjects:
Algorithms
Factor analysis
GDP
Gross Domestic Product
Maximum likelihood method
Methodology
Monte Carlo simulation
Statistical models
Studies
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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.
ISSN:0883-7252
1099-1255
DOI:10.1002/jae.2306