Abstract
The aim of this paper is to forecast monthly crude oil price with a hierarchical shrinkage approach, which utilizes not only LASSO for predictor selection, but a hierarchical Bayesian method to determine whether constant coefficient (CC) or time-varying parameter (TVP) predictive regression should be employed in each out-of-sample forecasting step. This newly developed method has the advantages of both model shrinkage and automatic switch between CC and TVP forecasting models; thus, this may produce more accurate predictions of crude oil prices. The empirical results show that this hierarchical shrinkage model can outperform many commonly used forecasting benchmark methods, such as AR, unobserved components stochastic volatility (UCSV), and multivariate regression models in forecasting crude oil price on various forecasting horizons.
Highlights
Crude oil price is one of the key indicators of the global macroeconomy and financial markets [1,2,3,4,5,6]
All the results are presented relative to the corresponding full model (LASSO on both constant coefficients and time-varying parameter (TVP)); smaller MAFE or mean of the squared forecast errors (MSFE), or larger mean of the log predictive likelihood (MLPL) than full model statistics indicate that the restricted model is forecasting better than the benchmark model
It is worth noting that the TVP regression models and constant coefficients models produce the worst forecasts in both cases according to MLPL. e results verified again that the new Bayesian hierarchical least absolute shrinkage and selection operator (LASSO) outperforms the traditional counterparts and enhances the prediction accuracy
Summary
Crude oil price is one of the key indicators of the global macroeconomy and financial markets [1,2,3,4,5,6]. A vast of literatures [2, 4, 5, 11, 13, 18, 20,21,22,23,24,25] indicate that except for previous oil prices, other parameters such as basic oil supply, demand and oil stock effects, financial market forces, market sentiment and uncertainty, macroeconomy, and geopolitical influences are main influencing factors If adding all these explanatory variables into the multivariate regression or autoregression (AR) class framework, it may lead to overfitting and misspecification problems and thereby constrain the forecast accuracy [7, 26, 27].
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