Slow Expectation-Maximization Convergence in Low-Noise Dynamic Factor Models

Daan Opschoor and Dick van Dijk
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Daan Opschoor: Erasmus University Rotterdam

No 23-018/III, Tinbergen Institute Discussion Papers from Tinbergen Institute

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.

Keywords: Dynamic factor models; EM algorithm; artificial noise; convergence speed; nowcasting (search for similar items in EconPapers)
JEL-codes: C32 C51 C53 E37 (search for similar items in EconPapers)
Date: 2023-04-05
New Economics Papers: this item is included in nep-ecm and nep-ets
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Persistent link: https://EconPapers.repec.org/RePEc:tin:wpaper:20230018

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