Discussion Paper Details

Please find the details for DP9334 in an easy to copy and paste format below:

Full Details   |   Bibliographic Reference

Full Details

Title: Short-term GDP forecasting with a mixed frequency dynamic factor model with stochastic volatility

Author(s): Massimiliano Marcellino, Mario Porqueddu and Fabrizio Venditti

Publication Date: February 2013

Keyword(s): Business cycle, Forecasting, Mixed-frequency data, Nonlinear models and Nowcasting

Programme Area(s): International Macroeconomics

Abstract: In this paper we develop a mixed frequency dynamic factor model featuring stochastic shifts in the volatility of both the latent common factor and the idiosyncratic components. We take a Bayesian perspective and derive a Gibbs sampler to obtain the posterior density of the model parameters. This new tool is then used to investigate business cycle dynamics and for forecasting GDP growth at short-term horizons in the euro area. We discuss three sets of empirical results. First we use the model to evaluate the impact of macroeconomic releases on point and density forecast accuracy and on the width of forecast intervals. Second, we show how our setup allows to make a probabilistic assessment of the contribution of releases to forecast revisions. Third we design a pseudo out of sample forecasting exercise and examine point and density forecast accuracy. In line with findings in the Bayesian Vector Autoregressions (BVAR) literature we find that stochastic volatility contributes to an improvement in density forecast accuracy.

For full details and related downloads, please visit:

Bibliographic Reference

Marcellino, M, Porqueddu, M and Venditti, F. 2013. 'Short-term GDP forecasting with a mixed frequency dynamic factor model with stochastic volatility'. London, Centre for Economic Policy Research.