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: Suggested Citation
Do you have negative results from your research you’d like to share?
Recommended Papers
-
The Generalized Dynamic Factor Model: One-Sided Estimation and Forecasting
By Mario Forni, Marc Hallin, ...
-
Arbitrage, Factor Structure, and Mean-Variance Analysis on Large Asset Markets
-
By James H. Stock and Mark W. Watson
-
Monetary Policy in a Data-Rich Environment
By Ben S. Bernanke and Jean Boivin
-
Eurocoin: A Real Time Coincident Indicator of the Euro Area Business Cycle
By Filippo Altissimo, Antonio Bassanetti, ...
-
Are More Data Always Better for Factor Analysis?
By Jean Boivin and Serena Ng
-
Implications of Dynamic Factor Models for VAR Analysis
By James H. Stock and Mark W. Watson
-
By Domenico Giannone, Lucrezia Reichlin, ...
-
By Domenico Giannone, Lucrezia Reichlin, ...