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

Marie Bessec () and Othman Bouabdallah
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Othman Bouabdallah: European Central Bank - European Central Bank

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Abstract: This paper merges two specifications recently developed in the forecasting literature: the MS-MIDAS model (Guérin and Marcellino, 2013) and the factor-MIDAS model (Marcellino and Schumacher, 2010). The MS-factor MIDAS model that we introduce incorporates the information provided by a large data set consisting of mixed frequency variables and captures regime-switching behaviours. Monte Carlo simulations show that this specification tracks the dynamics of the process and predicts the regime switches successfully, both in-sample and out-of-sample. We apply this model to US data from 1959 to 2010 and properly detect 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)
Date: 2015
Note: View the original document on HAL open archive server: https://hal.archives-ouvertes.fr/hal-01275760
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Published in Oxford Bulletin of Economics and Statistics, Wiley, 2015, 77 (3), ⟨10.1111/obes.12069⟩

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Journal Article: Forecasting GDP over the Business Cycle in a Multi-Frequency and Data-Rich Environment (2015) Downloads
Working Paper: Forecasting GDP over the business cycle in a multi-frequency and data-rich environment (2012) Downloads
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-01275760

DOI: 10.1111/obes.12069

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