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

33 Pages Posted: 25 Jun 2012

Date Written: June 1, 2012

Abstract

This paper merges two specifications developed recently in the forecasting literature: the MS-MIDAS model introduced by Gu´erin 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

JEL Classification: C22, E32, E37

Suggested Citation

Bessec, Marie and Bouabdallah, Othman, Forecasting GDP Over the Business Cycle in a Multi-Frequency and Data-Rich Environment (June 1, 2012). Banque de France Working Paper No. 384, Available at SSRN: https://ssrn.com/abstract=2090915 or http://dx.doi.org/10.2139/ssrn.2090915

Marie Bessec (Contact Author)

Banque de France ( email )

Paris
France

Othman Bouabdallah

Banque de France ( email )

Paris
France

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