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A Bayesian MIDAS Approach to Modeling First and Second Moment Dynamics

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  • Pettenuzzo, Davide
  • Timmermann, Allan G
  • Valkanov, Rossen

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.

Suggested Citation

  • Pettenuzzo, Davide & Timmermann, Allan G & Valkanov, Rossen, 2014. "A Bayesian MIDAS Approach to Modeling First and Second Moment Dynamics," CEPR Discussion Papers 10160, C.E.P.R. Discussion Papers.
  • Handle: RePEc:cpr:ceprdp:10160
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    References listed on IDEAS

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    Cited by:

    1. Roberto Casarin & Claudia Foroni & Massimiliano Marcellino & Francesco Ravazzolo, 2016. "Uncertainty Through the Lenses of A Mixed-Frequency Bayesian Panel Markov Switching Model," Working Papers 585, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    2. Ghysels, Eric, 2016. "Macroeconomics and the reality of mixed frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 294-314.

    More about this item

    Keywords

    Bayesian estimation; GDP growth; MIDAS regressions; out-of-sample forecasts; stochastic volatility;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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