Short-Term GDP Forecasting With a Mixed-Frequency Dynamic Factor Model With Stochastic Volatility
By Massimiliano Marcellino, M. Porqueddu, F. Venditti. 2016
166 citations recorded by the source corpus.
In this article, we develop a mixed frequency dynamic factor model in which the disturbances of both the latent common factor and of the idiosyncratic components have time-varying stochastic volatilities. We use the model to investigate business cycle dynamics in the euro area and present three sets of empirical results. First, we 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 examine point and density out of sample forecast accuracy. We find that introducing stochastic volatility in the model contributes to an improvement in both point and density forecast accuracy. Supplementary materials for this article are available online.
Subfields: Economics and Econometrics
View Paper: https://www.semanticscholar.org/paper/b78aef757fc2ac88f51d245e894a71f23d656894
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In Economics and Econometrics
Citations in the corpus
- A MIDAS Approach to Modeling First and Second Moment Dynamics (2016, unknown): “Marcellino et al. (2013) develop a mixed frequency dynamic factor model featuring stochastic shifts in the volatility of both the latent common factor and the idiosyncratic components.”
- Tracking the Slowdown in Long-Run GDP Growth (2017, methodology): “We obtain the DFM with SV of Marcellino et al. (2016) if we shut down time variation in the intercepts of the observables, that is, set r = m = 0 and ct = c.”