Abstract
ABSTRACT This study uses the recently proposed dynamic model averaging (DMA) and dynamic model selection (DMS) framework to develop forecasting models of Chinese soybean futures price with eight predictors, which allows both coefficients and forecasting models to evolve over time. Specifically, covering an out-of-sample period from August 2, 2005 to May 26, 2017, experimental results show that the DMA and DMS outperform the time-varying parameter model, autoregressive model, linear regression (including all predictors), and random walk on the basis of the standard accuracy measures and Diebold-Mariano (DM) test. The best predictors for forecasting soybean futures price tend to be time-varying. Policymakers and investors should realize that there are many potential predictors whose predictive powers are strong but vary over time in Chinese soybean futures price forecasting.
Published Version
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