Forecasting GDP over the business cycle in a multi-frequency and data-rich environment

Marie Bessec and O. Bouabdallah

Working papers from Banque de France

Abstract: This paper merges two specifications developed recently in the forecasting literature: the MS-MIDAS model introduced by Gu rin 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
Date: 2012
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Citations: View citations in EconPapers (1)

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Related works:
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)
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Persistent link: https://EconPapers.repec.org/RePEc:bfr:banfra:384

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