Forecasting GDP over the business cycle in a multi-frequency and data-rich environment
Marie Bessec () and
Working papers from Banque de France
This paper merges two specifications developed recently in the forecasting literature: the MS-MIDAS model introduced by Guérin and Marcellino (2011) and the MIDAS-factor model considered in Marcellino and Schumacher (2010). The MS-factor MIDAS model (MS-FaMIDAS) that we introduce incorporates the information provided by a large data-set, takes into account mixed frequency variables and captures regime-switching behaviors. Monte Carlo simulations show that this new specification tracks the dynamics of the process quite well and predicts the regime switches successfully, both in sample and out-of-sample. We apply this new model to US data from 1959 to 2010 and detect properly the US recessions by exploiting the link between GDP growth and higher frequency financial variables.
Keywords: Markov-Switching; factor models; mixed frequency data; GDP forecasting. (search for similar items in EconPapers)
JEL-codes: C22 E32 E37 (search for similar items in EconPapers)
Pages: 33 pages
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed
Downloads: (external link)
https://publications.banque-france.fr/sites/defaul ... g-paper_384_2012.pdf (application/pdf)
Journal Article: Forecasting GDP over the Business Cycle in a Multi-Frequency and Data-Rich Environment (2015)
Working Paper: Forecasting GDP over the business cycle in a multi-frequency and data-rich environment (2015)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:bfr:banfra:384
Access Statistics for this paper
More papers in Working papers from Banque de France Banque de France 31 Rue Croix des Petits Champs LABOLOG - 49-1404 75049 PARIS. Contact information at EDIRC.
Bibliographic data for series maintained by Michael brassart ().