Stock returns and real growth: A Bayesian nonparametric approach
Journal of Empirical Finance, 2019, vol. 53, issue C, 53-69
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: C58 C14 C22 C11 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:empfin:v:53:y:2019:i:c:p:53-69
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Journal of Empirical Finance is currently edited by R. T. Baillie, F. C. Palm, Th. J. Vermaelen and C. C. P. Wolff
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