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A spectral EM algorithm for dynamic factor models

Gabriele Fiorentini (), Alessandro Galesi () and Enrique Sentana ()

No 10417, CEPR Discussion Papers from C.E.P.R. Discussion Papers

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 (search for similar items in EconPapers)
JEL-codes: C32 C38 C51 (search for similar items in EconPapers)
Date: 2015-02
New Economics Papers: this item is included in nep-ets and nep-ore
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Related works:
Journal Article: A spectral EM algorithm for dynamic factor models (2018) Downloads
Working Paper: A spectral EM algorithm for dynamic factor models (2016) Downloads
Working Paper: A Spectral EM Algorithm for Dynamic Factor Models (2014) Downloads
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