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Forecasting GDP over the Business Cycle in a Multi-Frequency and Data-Rich Environment

Marie Bessec () and Othman Bouabdallah

Oxford Bulletin of Economics and Statistics, 2015, vol. 77, issue 3, 360-384

Abstract: type="main" xml:id="obes12069-abs-0001">

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.

Date: 2015
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Related works:
Working Paper: 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 (2012) Downloads
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