Factor extraction using Kalman filter and smoothing: This is not just another survey
EntityUAM. Departamento de Análisis Económico: Economía Cuantitativa
10.1016/j.ijforecast.2021.01.027International Journal Of Forecasting 37.4 (2021): 1399-1425
ISSN0169-2070 (print); 1872-8200 (online)
Funded byFinancial support from the Spanish Government Project PID2019-108079GB-C22/AEI/10.13039/501100011033 (MINECO/FEDER) is gratefully acknowledged by Pilar Poncela. Esther Ruiz and Karen Miranda acknowledge financial support from project PID2019-108079GB-C21 (MINECO/FEDER)
ProjectGobierno de España. PID2019-108079GB-C22/AEI/10.13039/501100011033
SubjectsDynamic factor model; Expectation maximization algorithm; Identification; Macroeconomic forecasting; State-space model; Economía
Rights© 2021 International Institute of Forecasters
Esta obra está bajo una Licencia Creative Commons Atribución 4.0 Internacional.
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
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