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Forecasting GDP over the Business Cycle in a Multi‐Frequency and Data‐Rich Environment
Author(s) -
Bessec Marie,
Bouabdallah Othman
Publication year - 2015
Publication title -
oxford bulletin of economics and statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.131
H-Index - 73
eISSN - 1468-0084
pISSN - 0305-9049
DOI - 10.1111/obes.12069
Subject(s) - business cycle , recession , monte carlo method , sample (material) , econometrics , data set , computer science , dynamic factor , set (abstract data type) , great recession , process (computing) , factor (programming language) , economics , statistics , mathematics , macroeconomics , artificial intelligence , keynesian economics , chemistry , chromatography , programming language , operating system
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.