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Maximum likelihood estimation of factor models on data sets with arbitrary pattern of missing data

Marta Banbura () and Michele Modugno ()

No 1189, Working Paper Series from European Central Bank

Abstract: In this paper we propose a methodology to estimate a dynamic factor model on data sets with an arbitrary pattern of missing data. We modify the Expectation Maximisation (EM) algorithm as proposed for a dynamic factor model by Watson and Engle (1983) to the case with general pattern of missing data. We also extend the model to the case with serially correlated idiosyncratic component. The framework allows to handle efficiently and in an automatic manner sets of indicators characterized by different publication delays, frequencies and sample lengths. This can be relevant e.g. for young economies for which many indicators are compiled only since recently. We also show how to extract a model based news from a statistical data release within our framework and we derive the relationship between the news and the resulting forecast revision. This can be used for interpretation in e.g. nowcasting applications as it allows to determine the sign and size of a news as well as its contribution to the revision, in particular in case of simultaneous data releases. We evaluate the methodology in a Monte Carlo experiment and we apply it to nowcasting and backdating of euro area GDP. JEL Classification: C53, E37

Keywords: EM algorithm; factor models; forecasting; large cross-sections; Missing data (search for similar items in EconPapers)
Date: 2010-05
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
Note: 810771
References: View complete reference list from CitEc
Citations: View citations in EconPapers (66) Track citations by RSS feed

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Related works:
Journal Article: MAXIMUM LIKELIHOOD ESTIMATION OF FACTOR MODELS ON DATASETS WITH ARBITRARY PATTERN OF MISSING DATA (2014) Downloads
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