A Bayesian MIDAS Approach to Modeling First and Second Moment Dynamics

Davide Pettenuzzo (), Rossen Valkanov and Allan Timmermann
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Allan Timmermann: University of California San Diego

No 76, Working Papers from Brandeis University, Department of Economics and International Business School

Abstract: We propose a new approach to predictive density modeling that allows for MI- DAS e¤ects in both the ?rst and second moments of the outcome and develop Gibbs sampling methods for Bayesian estimation in the presence of stochastic volatility dy- namics. When applied to quarterly U.S. GDP growth data, we ?nd strong evidence that models that feature MIDAS terms in the conditional volatility generate more accurate forecasts than conventional benchmarks. Finally, we ?nd 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: MIDAS regressions; Bayesian estimation; stochastic volatility; out- of-sample forecasts; GDP growth. (search for similar items in EconPapers)
JEL-codes: C11 C32 C53 E37 (search for similar items in EconPapers)
Pages: 59 pages
Date: 2014-07
New Economics Papers: this item is included in 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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http://www.brandeis.edu/economics/RePEc/brd/doc/Brandeis_WP76.pdf First version, 2014 (application/pdf)

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Working Paper: A Bayesian MIDAS Approach to Modeling First and Second Moment Dynamics (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:brd:wpaper:76

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