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Factor extraction using Kalman filter and smoothing: this is not just another survey

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URI: http://hdl.handle.net/10016/30644
ISSN: 2387-0303
UXXI: DT/0000001767
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ws202005.pdf (1.84 MB)
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2020-06-25
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Abstract
Dynamic Factor Models, which assume the existence of a small number of unobservedlatent factors that capture the comovements in a system of variables, are the main "bigdata" tool used by empirical macroeconomists during the last 30 years. One importanttool to extract the factors is based on Kalman lter and smoothing procedures that cancope with missing data, mixed frequency data, time-varying parameters, non-linearities,non-stationarity and many other characteristics often observed in real systems of economicvariables. This paper surveys the literature on latent common factors extracted using Kalmanfilter and smoothing procedures in the context of Dynamic Factor Models. Signal extractionand parameter estimation issues are separately analyzed. Identi cation issues are also tackledin both stationary and non-stationary models. Finally, empirical applications are surveyedin both cases.
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Dynamic Factor Model, Em Algorithm, Identi Cation, State-Space Model
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DES - Working Papers. Statistics and Econometrics. WS