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A spectral EM algorithm for dynamic factor models

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

Journal of Econometrics, 2018, vol. 205, issue 1, 249-279

Abstract: We make two complementary contributions to efficiently estimate dynamic factor models: a frequency domain EM algorithm and a swift iterated indirect inference procedure for ARMA models with no asymptotic efficiency loss for any finite number of iterations. Although our procedures can estimate such models with many series without good initial values, near the optimum we recommend switching to a gradient method that analytically computes spectral scores using the EM principle. We successfully employ our methods to construct an index that captures the common movements of US sectoral employment growth rates, which we compare to the indices obtained by semiparametric methods.

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: 2018
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Related works:
Working Paper: A spectral EM algorithm for dynamic factor models (2016) Downloads
Working Paper: A spectral EM algorithm for dynamic factor models (2015) Downloads
Working Paper: A Spectral EM Algorithm for Dynamic Factor Models (2014) Downloads
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DOI: 10.1016/j.jeconom.2018.03.013

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Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson

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