Determining Number of Factors in Dynamic Factor Models Contributing to GDP Nowcasting

Real-time nowcasting is a process to assess current-quarter GDP from timely released economic and financial series before the figure is disseminated in order to catch the overall macroeconomic conditions in real time. In economic data nowcasting, dynamic factor models (DFMs) are widely used due to t...

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Autores principales:	Jiayi Luo, Cindy Long Yu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	Bayesian analysis dynamic factor models horseshoe shrinkage nowcasting Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/618d67125bcc4e26aad246db239d90b7
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spelling	oai:doaj.org-article:618d67125bcc4e26aad246db239d90b72021-11-25T18:16:41ZDetermining Number of Factors in Dynamic Factor Models Contributing to GDP Nowcasting10.3390/math92228652227-7390https://doaj.org/article/618d67125bcc4e26aad246db239d90b72021-11-01T00:00:00Zhttps://www.mdpi.com/2227-7390/9/22/2865https://doaj.org/toc/2227-7390Real-time nowcasting is a process to assess current-quarter GDP from timely released economic and financial series before the figure is disseminated in order to catch the overall macroeconomic conditions in real time. In economic data nowcasting, dynamic factor models (DFMs) are widely used due to their abilities to bridge information with different frequencies and to achieve dimension reduction. However, most of the research using DFMs assumes a fixed known number of factors contributing to GDP nowcasting. In this paper, we propose a Bayesian approach with the horseshoe shrinkage prior to determine the number of factors that have nowcasting power in GDP and to accurately estimate model parameters and latent factors simultaneously. The horseshoe prior is a powerful shrinkage prior in that it can shrink unimportant signals to 0 while keeping important ones remaining large and practically unshrunk. The validity of the method is demonstrated through simulation studies and an empirical study of nowcasting U.S. quarterly GDP growth rates using monthly data series in the U.S. market.Jiayi LuoCindy Long YuMDPI AGarticleBayesian analysisdynamic factor modelshorseshoe shrinkagenowcastingMathematicsQA1-939ENMathematics, Vol 9, Iss 2865, p 2865 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Bayesian analysis dynamic factor models horseshoe shrinkage nowcasting Mathematics QA1-939
spellingShingle	Bayesian analysis dynamic factor models horseshoe shrinkage nowcasting Mathematics QA1-939 Jiayi Luo Cindy Long Yu Determining Number of Factors in Dynamic Factor Models Contributing to GDP Nowcasting
description	Real-time nowcasting is a process to assess current-quarter GDP from timely released economic and financial series before the figure is disseminated in order to catch the overall macroeconomic conditions in real time. In economic data nowcasting, dynamic factor models (DFMs) are widely used due to their abilities to bridge information with different frequencies and to achieve dimension reduction. However, most of the research using DFMs assumes a fixed known number of factors contributing to GDP nowcasting. In this paper, we propose a Bayesian approach with the horseshoe shrinkage prior to determine the number of factors that have nowcasting power in GDP and to accurately estimate model parameters and latent factors simultaneously. The horseshoe prior is a powerful shrinkage prior in that it can shrink unimportant signals to 0 while keeping important ones remaining large and practically unshrunk. The validity of the method is demonstrated through simulation studies and an empirical study of nowcasting U.S. quarterly GDP growth rates using monthly data series in the U.S. market.
format	article
author	Jiayi Luo Cindy Long Yu
author_facet	Jiayi Luo Cindy Long Yu
author_sort	Jiayi Luo
title	Determining Number of Factors in Dynamic Factor Models Contributing to GDP Nowcasting
title_short	Determining Number of Factors in Dynamic Factor Models Contributing to GDP Nowcasting
title_full	Determining Number of Factors in Dynamic Factor Models Contributing to GDP Nowcasting
title_fullStr	Determining Number of Factors in Dynamic Factor Models Contributing to GDP Nowcasting
title_full_unstemmed	Determining Number of Factors in Dynamic Factor Models Contributing to GDP Nowcasting
title_sort	determining number of factors in dynamic factor models contributing to gdp nowcasting
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/618d67125bcc4e26aad246db239d90b7
work_keys_str_mv	AT jiayiluo determiningnumberoffactorsindynamicfactormodelscontributingtogdpnowcasting AT cindylongyu determiningnumberoffactorsindynamicfactormodelscontributingtogdpnowcasting
_version_	1718411384159469568

Determining Number of Factors in Dynamic Factor Models Contributing to GDP Nowcasting

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