A Bayesian MIDAS Approach to Modeling First and Second Moment Dynamics

Allan Timmermann, Davide Pettenuzzo () and Rossen Valkanov

No 10160, CEPR Discussion Papers from C.E.P.R. Discussion Papers

Abstract: We propose a new approach to predictive density modeling that allows for MIDAS effects in both the first and second moments of the outcome and develop Gibbs sampling methods for Bayesian estimation in the presence of stochastic volatility dynamics. When applied to quarterly U.S. GDP growth data, we find strong evidence that models that feature MIDAS terms in the conditional volatility generate more accurate forecasts than conventional benchmarks. Finally, we find that forecast combination methods such as the optimal predictive pool of Geweke and Amisano (2011) produce consistent gains in out-of-sample predictive performance.

Keywords: Bayesian estimation; Gdp growth; Midas regressions; Out-of-sample forecasts; stochastic volatility (search for similar items in EconPapers)
JEL-codes: C11 C32 C53 E37 (search for similar items in EconPapers)
Date: 2014-09
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for, nep-mac and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Working Paper: A Bayesian MIDAS Approach to Modeling First and Second Moment Dynamics (2014)
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