Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/273829 
Year of Publication: 
2023
Series/Report no.: 
Tinbergen Institute Discussion Paper No. TI 2023-018/III
Publisher: 
Tinbergen Institute, Amsterdam and Rotterdam
Abstract: 
This paper addresses the poor performance of the Expectation-Maximization (EM) algorithm in the estimation of low-noise dynamic factor models, commonly used in macroeconomic forecasting and nowcasting. We show analytically and in Monte Carlo simulations how the EM algorithm stagnates in a low-noise environment, leading to inaccurate estimates of factor loadings and latent factors. An adaptive version of EM considerably speeds up convergence, producing substantial improvements in estimation accuracy. Modestly increasing the noise level also accelerates convergence. A nowcasting exercise of euro area GDP growth shows gains up to 34% by using adaptive EM relative to the usual EM.
Subjects: 
Dynamic factor models
EM algorithm
artificial noise
convergence speed
nowcasting
JEL: 
C32
C51
C53
E37
Document Type: 
Working Paper

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