Factor models for large and incomplete data sets with unknown group structure

Camacho, Maximo; Lopez-Buenache, German

Publication:
Factor models for large and incomplete data sets with unknown group structure

dc.contributor.author	Camacho, Maximo
dc.contributor.author	Lopez-Buenache, German
dc.contributor.department	Métodos Cuantitativos para la Economía y la Empresa
dc.date.accessioned	2024-01-11T11:42:58Z
dc.date.available	2024-01-11T11:42:58Z
dc.date.issued	2023-07
dc.description	© 2022 The Authors. Published by Elsevier B.V. on behalf of International Institute of Forecasters.This document is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ This document is the submitted version of a published work that appeared in final form in International Journal of Forecasting.	es
dc.description.abstract	Most economic applications rely on a large number of time series, which typically have a remarkable clustering structure and they are available over different spans. To handle these databases, we combined the expectation–maximization (EM) algorithm outlined by Stock and Watson (JBES, 2002) and the estimation algorithm for large factor models with an unknown number of group structures and unknown membership described by Ando and Bai (JAE, 2016; JASA, 2017) . Several Monte Carlo experiments demonstrated the good performance of the proposed method at determining the correct number of clusters, providing the appropriate number of group-specific factors, identifying error-free group membership, and obtaining accurate estimates of unobserved missing data. In addition, we found that our proposed method performed substantially better than the standard EM algorithm when the data had a grouped factor structure. Using the Federal Reserve Economic Data FRED-QD, our method detected two distinct groups of macroeconomic indicators comprising the real activity indicators and nominal indicators. Thus, we demonstrated the usefulness of our group-specific factor model for studies of business cycle chronology and for forecasting purposes.	es
dc.format	application/pdf	es
dc.format.extent	31	es
dc.identifier.citation	International Journal of Forecasting. Volume 39, Issue 3, July–September 2023, Pages 1205-1220
dc.identifier.doi	https://doi.org/10.1016/j.ijforecast.2022.05.012
dc.identifier.issn	0169-2070
dc.identifier.uri	http://hdl.handle.net/10201/137183
dc.language	eng	es
dc.publisher	Elsevier B.V. on behalf of International Institute of Forecasters.	es
dc.relation	PID2019-107192GB-I00	es
dc.relation.publisherversion	https://www.sciencedirect.com/science/article/pii/S0169207022000723	es
dc.relation.replaces	https://www.um.es/econometria/Maximo/articulos/MissGroup.pdf	es
dc.rights	info:eu-repo/semantics/openAccess	es
dc.rights	Attribution-NonCommercial-NoDerivatives 4.0 Internacional	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/4.0/	*
dc.subject	Factor models	es
dc.subject	Big data analysis	es
dc.subject	Business cycles	es
dc.subject	Missing data	es
dc.subject	Forecasting	es
dc.subject.other	CDU::3 - Ciencias sociales	es
dc.title	Factor models for large and incomplete data sets with unknown group structure	es
dc.type	info:eu-repo/semantics/article	es
dspace.entity.type	Publication	es