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

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dc.contributor.author Ruiz Ortega, Esther
dc.contributor.author Miranda Gualdrón, Karen Alejandra
dc.contributor.author Poncela Blanco, Maria Pilar
dc.date.accessioned 2022-03-14T16:36:57Z
dc.date.available 2022-03-14T16:36:57Z
dc.date.issued 2021-10-01
dc.identifier.bibliographicCitation 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.
dc.identifier.issn 0169-2070
dc.identifier.uri http://hdl.handle.net/10016/34366
dc.description.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
dc.description.sponsorship 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).
dc.language.iso eng
dc.publisher Elsevier
dc.rights ©2021 International Institute of Forecasters.
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other Dynamic factor model
dc.subject.other Expectation maximization algorithm
dc.subject.other Identification
dc.subject.other Macroeconomic forecasting
dc.subject.other State-space model
dc.title Factor extraction using Kalman filter and smoothing: This is not just another survey
dc.type article
dc.subject.eciencia Estadística
dc.identifier.doi https://doi.org/10.1016/j.ijforecast.2021.01.027
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. PID2019-108079GB-C21
dc.type.version publishedVersion
dc.identifier.publicationfirstpage 1399
dc.identifier.publicationissue 4
dc.identifier.publicationlastpage 1425
dc.identifier.publicationtitle INTERNATIONAL JOURNAL OF FORECASTING
dc.identifier.publicationvolume 37
dc.identifier.uxxi AR/0000030263
dc.contributor.funder Agencia Estatal de Investigación (España)
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