A MIDAS approach to modeling first and second moment dynamics

Davide Pettenuzzo (), Allan Timmermann and Rossen Valkanov

Journal of Econometrics, 2016, vol. 193, issue 2, 315-334

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. Specifically, our modeling approach allows for MIDAS stochastic volatility dynamics, generalizing a large literature focusing on MIDAS effects in the conditional mean, and allows the models to be estimated by means of standard Gibbs sampling methods. When applied to monthly time series on growth in industrial production and inflation, we find strong evidence that the introduction of MIDAS effects in the volatility equation leads to improved in-sample and out-of-sample density forecasts. Our results also suggest that model combination schemes assign high weight to MIDAS-in-volatility models and produce consistent gains in out-of-sample predictive performance.

Keywords: MIDAS regressions; Bayesian estimation; Stochastic volatility; Out-of-sample forecasts; Inflation forecasts; Industrial production (search for similar items in EconPapers)
JEL-codes: C11 C32 C53 E37 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (43)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:193:y:2016:i:2:p:315-334

DOI: 10.1016/j.jeconom.2016.04.009

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