Stock Returns and Real Growth: A Bayesian Nonparametric Approach
28 Pages Posted: 10 Apr 2018 Last revised: 26 Jun 2019
Date Written: June 25, 2019
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 switch- ing, MCMC
JEL Classification: C58, C14, C22, C11
Suggested Citation: Suggested Citation