| By: |
Massimiliano Marcellino (European University Institute and Bocconi University);
Mario Porqueddu (Bank of Italy);
Fabrizio Venditti (Bank of Italy) |
| 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 to forecast 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 us 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 literature on Bayesian
Vector Autoregressions (BVAR), we find that stochastic volatility contributes
to an improvement in density forecast accuracy. |
| Keywords: |
forecasting, business cycle, mixed-frequency data, nonlinear models, nowcasting |
| JEL: |
E32 C22 E27 |
| Date: |
2013–01 |
| URL: |
http://d.repec.org/n?u=RePEc:bdi:wptemi:td_896_13&r=mst |