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Discussion Paper Details

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Title: A spectral EM algorithm for dynamic factor models

Author(s): Gabriele Fiorentini, Alessandro Galesi and Enrique Sentana

Publication Date: February 2015

Keyword(s): Indirect inference, Kalman filter, Sectoral employment, Spectral maximum likelihood and Wiener-Kolmogorov filter

Programme Area(s): International Macroeconomics

Abstract: We introduce a frequency domain version of the EM algorithm for general dynamic factor models. We consider both AR and ARMA processes, for which we develop iterative indirect inference procedures analogous to the algorithms in Hannan (1969). Although our proposed procedure allows researchers to estimate such models by maximum likelihood with many series even without good initial values, we recommend switching to a gradient method that uses the EM principle to swiftly compute frequency domain analytical scores near the optimum. We successfully employ our algorithm to construct an index that captures the common movements of US sectoral employment growth rates.

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Bibliographic Reference

Fiorentini, G, Galesi, A and Sentana, E. 2015. 'A spectral EM algorithm for dynamic factor models'. London, Centre for Economic Policy Research. https://cepr.org/active/publications/discussion_papers/dp.php?dpno=10417