A Spectral EM Algorithm for Dynamic Factor Models

45 Pages Posted: 17 Feb 2015

See all articles by Gabriele Fiorentini

Gabriele Fiorentini

Universita di Firenze - Dipartimento di Statistica

Alessandro Galesi

idealista

Enrique Sentana

Centro de Estudios Monetarios y Financieros (CEMFI); Financial Markets Group; Centre for Economic Policy Research (CEPR)

Multiple version iconThere are 2 versions of this paper

Date Written: February 2015

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.

Keywords: Indirect inference, Kalman filter, Sectoral employment, Spectral maximum likelihood, Wiener-Kolmogorov filter

JEL Classification: C32, C38, C51

Suggested Citation

Fiorentini, Gabriele and Galesi, Alessandro and Sentana, Enrique, A Spectral EM Algorithm for Dynamic Factor Models (February 2015). CEPR Discussion Paper No. DP10417, Available at SSRN: https://ssrn.com/abstract=2566320

Gabriele Fiorentini (Contact Author)

Universita di Firenze - Dipartimento di Statistica ( email )

Viale Morgagni, 59
50134 Firenze
Italy
+39 055 4237 274 (Phone)
+39 055 4223 560 (Fax)

Alessandro Galesi

idealista ( email )

Madrid, Madrid 28014
Spain

Enrique Sentana

Centro de Estudios Monetarios y Financieros (CEMFI) ( email )

Casado del Alisal 5
28014 Madrid
Spain
+34 91 429 0551 (Phone)
+34 91 429 1056 (Fax)

HOME PAGE: http://www.cemfi.es/~sentana/

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Centre for Economic Policy Research (CEPR)

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United Kingdom

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