Dynamic factor models have been the main “big data” tool used by empirical macroeconomists during the last 30 years. In this context, Kalman filter and smoothing (KFS) procedures can cope with missing data, mixed frequency data, time-varying parameters, non-linearities, non-stationarity, and many other characteristics often observed in real systems of economic variables. The main contribution of this paper is to provide a comprehensive updated summary of the literature on latent common factors extracted using KFS procedures in the context of dynamic factor models, pointing out their potential limitations. Signal extraction and parameter estimation issues are separately analyzed. Identification issues are also tackled in both stationary and non-stationary models. Finally, empirical applications are surveyed in both cases. This survey is relevant to researchers and practitioners interested not only in the theory of KFS procedures for factor extraction in dynamic factor models but also in their empirical application in macroeconomics and finance.

在过去的 30 年里，动态因子模型一直是实证宏观经济学家使用的主要“大数据”工具。在这种情况下，卡尔曼滤波和平滑 (KFS) 程序可以处理缺失数据、混合频率数据、时变参数、非线性、非平稳性以及在真实经济变量系统中经常观察到的许多其他特征。本文的主要贡献是提供有关在动态因子模型背景下使用 KFS 程序提取的潜在公共因子的文献的全面更新总结，指出它们的潜在局限性。分别分析了信号提取和参数估计问题。识别问题也在平稳和非平稳模型中得到解决。最后，在这两种情况下都对实证应用进行了调查。