Short-term GDP forecasting with a mixed frequency dynamic factor model with stochastic volatility
Mario Porqueddu and
Fabrizio Venditti ()
No 9334, CEPR Discussion Papers from C.E.P.R. Discussion Papers
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 (search for similar items in EconPapers)
JEL-codes: C22 E27 E32 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-for and nep-mst
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Journal Article: Short-Term GDP Forecasting With a Mixed-Frequency Dynamic Factor Model With Stochastic Volatility (2016)
Working Paper: Short-term GDP forecasting with a mixed frequency dynamic factor model with stochastic volatility (2013)
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