Stock Returns and Real Growth : A Bayesian Nonparametric Approach

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

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Year of publication:	2019
Authors:	Yang, Qiao
Publisher:	[2019]: [S.l.] : SSRN
Subject:	Kapitaleinkommen \| Capital income \| Wirtschaftswachstum \| Economic growth \| Nichtparametrisches Verfahren \| Nonparametric statistics \| Theorie \| Theory \| Bayes-Statistik \| Bayesian inference

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