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Estimating a Dynamic Factor Model in EViews Using the Kalman Filter and Smoother
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
Statistiska institutionen, Stockholms universitet.
(English)In: Computational Economics, ISSN 0927-7099, E-ISSN 1572-9974Article in journal (Refereed) Epub ahead of print
Abstract [en]

Dynamic factor models have become very popular for analyzing high-dimensional time series, and are now standard tools in, for instance, business cycle analysis and forecasting. Despite their popularity, most statistical software do not provide these models within standard packages. We briefly review the literature and show how to estimate a dynamic factor model in EViews. A subroutine that estimates the model is provided. In a simulation study, the precision of the estimated factors are evaluated, and in an empirical example, the usefulness of the model is illustrated.

Keywords [en]
Dynamic factor model, State space, Kalman filter, EViews
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
URN: urn:nbn:se:uu:diva-392549DOI: 10.1007/s10614-019-09912-zOAI: oai:DiVA.org:uu-392549DiVA, id: diva2:1348897
Available from: 2019-09-05 Created: 2019-09-05 Last updated: 2019-09-05

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Publisher's full texthttps://link.springer.com/article/10.1007/s10614-019-09912-z

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