Working Paper: CEPR ID: DP9334
Authors: Massimiliano Marcellino; Mario Porqueddu; Fabrizio Venditti
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.
Keywords: business cycle forecasting; mixed-frequency data; nonlinear models; nowcasting
JEL Codes: C22; E27; E32
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| mixed frequency dynamic factor model (C22) | accuracy of GDP growth forecasts (H68) |
| stochastic volatility (C58) | point and density forecast accuracy (C53) |
| macroeconomic releases (E60) | forecast revisions (C53) |
| macroeconomic releases (E60) | GDP growth estimates (O49) |
| economic shocks (F69) | parameter instability (C62) |