Working Paper: CEPR ID: DP10417
Authors: Gabriele Fiorentini; Alessandro Galesi; Enrique Sentana
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 Codes: C32; C38; C51
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| common factor (xt) (C29) | sectoral employment growth rates (yt) (J49) |