Infinite Markov pooling of predictive distributions

Xin Jin, John Maheu and Qiao Yang

Journal of Econometrics, 2022, vol. 228, issue 2, 302-321

Abstract: This paper introduces novel approaches to forecast pooling methods based on a nonparametric prior for a weight vector combining predictive densities. The first approach places a Dirichlet process prior on the weight vector and generalizes the static linear pool. The second approach uses a hierarchical Dirichlet process prior to allow the weight vector to follow an infinite hidden Markov chain. This generalizes dynamic prediction pools to the nonparametric setting. Efficient posterior simulation based on MCMC methods is also examined. Detailed applications to short-term interest rates, realized covariance matrices and asset pricing models demonstrate that the nonparametric pool forecasts well. The paper concludes with extensions and an application for calibrating and combining predictive densities.

Keywords: Prediction pools; Dirichlet process; Beam sampling; Infinite Markov switching; Density forecast; Short-term interest rates; Realized covariance matrices (search for similar items in EconPapers)
JEL-codes: C11 C32 C53 Q43 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:228:y:2022:i:2:p:302-321

DOI: 10.1016/j.jeconom.2021.10.010

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