.png){kind=icon}
.png){kind=icon}
Return
A spectral EM algorithm for dynamic factor models
.png){kind=icon}
OA
AI
DOI：10.1016/j.jeconom.2018.03.013.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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. (C) 2018 Elsevier B.V. All rights reserved.
Keywords:
Indirect inference
Kalman filter
Sectoral employment
Spectral maximum likelihood
Wiener-Kolmogorov filter
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.