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

Ruiz Ortega, Esther; Miranda Gualdrón, Karen Alejandra; Poncela Blanco, Maria Pilar

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

Author(s): Ruiz Ortega, Esther; Miranda Gualdrón, Karen Alejandra; Poncela Blanco, Maria Pilar

Publisher: Elsevier

Issued date: 2021-10-01

Citation: Poncela, P., Ruiz, E., & Miranda, K. (2021). Factor extraction using Kalman filter and smoothing: This is not just another survey. En International Journal of Forecasting, 37 (4), pp. 1399-1425.

ISSN: 0169-2070

DOI: https://doi.org/10.1016/j.ijforecast.2021.01.027

xmlui.dri2xhtml.METS-1.0.item-contributor-funder: Agencia Estatal de Investigación (España)

Sponsor: Financial 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).

Project: Gobierno de España. PID2019-108079GB-C21

Keywords: Dynamic factor model , Expectation maximization algorithm , Identification , Macroeconomic forecasting , State-space model

URI: http://hdl.handle.net/10016/34366

Rights: ©2021 International Institute of Forecasters.
Atribución-NoComercial-SinDerivadas 3.0 España

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, n [+]

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