A Bayesian Nonparametric Investigation of the Predictive Effect of Exchange Rates on Commodity Prices

Xin Jin ()
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Xin Jin: School of Economics, Shanghai University of Finance and Economics (SUFE), Shanghai 200433, China; Key Laboratory of Mathematical Economics (SUFE), Ministry of Education, Shanghai 200433, China

Frontiers of Economics in China-Selected Publications from Chinese Universities, 2020, vol. 15, issue 2, 179-210

Abstract: This study proposes a full Bayesian nonparametric procedure to investigate the predictive power of exchange rates in relation to commodity prices for three commodity-exporting countries: Canada, Australia, and New Zealand. We propose a new time-dependent infinite mixture of a normal linear regression model of the conditional distribution of the commodity price index. The mixing weights follow a set of Probit stick-breaking priors that are time-varying. We find that exchange rates have a positive predictive effect in general, but accounting for time variation does not improve forecasting performance. By contrast, the intercept in the regression and the lagged dependent variable show signs of parameter change over time in most cases, which is important in forecasting both the mean and the density of commodity prices one period ahead. The results also suggest that the variance is a large source of the time variation in the conditional distribution of commodity prices.

Keywords: Bayesian nonparametrics; Dirichlet process mixture; stick-breaking process; Markov China Monte Carlo (MCMC); predictive likelihood; foreign exchange rate; commodity price (search for similar items in EconPapers)
JEL-codes: C11 C14 C22 F47 (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:fec:journl:v:15:y:2020:i:2:p:179-210

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