Factor extraction using Kalman filter and smoothing: This is not just another survey Articles uri icon

publication date

  • October 2021

start page

  • 1399

end page

  • 1425

issue

  • 4

volume

  • 37

International Standard Serial Number (ISSN)

  • 0169-2070

Electronic International Standard Serial Number (EISSN)

  • 1872-8200

abstract

  • 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.

keywords

  • dynamic factor model; expectation maximization algorithm; identification; macroeconomic forecasting; state-space model