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

Author

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  • Daan Opschoor

    (Erasmus University Rotterdam)

  • Dick van Dijk

    (Erasmus University 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.

Suggested Citation

  • Daan Opschoor & Dick van Dijk, 2023. "Slow Expectation-Maximization Convergence in Low-Noise Dynamic Factor Models," Tinbergen Institute Discussion Papers 23-018/III, Tinbergen Institute.
    • Handle: RePEc:tin:wpaper:20230018
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    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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