Slow Expectation–Maximization Convergence in Low‐Noise Dynamic Factor Models
研究了低噪声动态因子模型中EM算法收敛慢的问题，提出自适应EM算法，在欧元区GDP增长预测中均方根误差降低高达34%。
ABSTRACT This paper addresses the slow convergence of the expectation–maximization (EM) algorithm in the estimation of low‐noise dynamic factor models, commonly used in macroeconomic nowcasting and forecasting. 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 factor realizations. An adaptive version of EM considerably speeds up convergence, producing substantial improvements in estimation accuracy. An empirical nowcasting exercise of euro area GDP growth shows gains in root mean squared forecast error up to 34% by using the adaptive EM relative to the standard algorithm.