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

Massimiliano Marcellino, Mario Porqueddu () and Fabrizio Venditti
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Mario Porqueddu: Bank of Italy

No 896, Temi di discussione (Economic working papers) from Bank of Italy, Economic Research and International Relations Area

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 (search for similar items in EconPapers)
JEL-codes: C22 E27 E32 (search for similar items in EconPapers)
Date: 2013-01
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for, nep-mac and nep-mst
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Citations: View citations in EconPapers (24)

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Related works:
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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Persistent link: https://EconPapers.repec.org/RePEc:bdi:wptemi:td_896_13

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