Stock returns and real growth: A Bayesian nonparametric approach

Qiao Yang

Journal of Empirical Finance, 2019, vol. 53, issue C, 53-69

Abstract: This study constructs a Bayesian nonparametric model to investigate whether stock market returns predict real economic growth. Unlike earlier studies, our use of an infinite hidden Markov model enables parameters to be time-varying across an infinite number of Markov-switching states estimated from data rather than fixed like a prior. Our model exhibits significantly greater accuracy in out-of-sample density forecasts. We uncover strong evidence of the time-varying power of lagged stock returns to predict economic growth.

Keywords: Hierarchical dirichlet process prior; Beam sampling; Markov-switching; MCMC (search for similar items in EconPapers)
JEL-codes: C11 C14 C22 C58 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:empfin:v:53:y:2019:i:c:p:53-69

DOI: 10.1016/j.jempfin.2019.06.005

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