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.

A spectral EM algorithm for dynamic factor models, Centre for Economic Policy Research DP10417, ISSN: 0265-8003 / Gabriele Fiorentini; Alessandro Galei; Enrique Sentana. - STAMPA. - (2015), pp. 1-42.

A spectral EM algorithm for dynamic factor models, Centre for Economic Policy Research DP10417, ISSN: 0265-8003

Gabriele Fiorentini;
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.
2015
Gabriele Fiorentini; Alessandro Galei; Enrique Sentana...espandi
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1148605
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