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.

中文翻译：

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动态参数模型中的递减收缩预测油价。

本文的目的是通过分级收缩方法来预测月度原油价格，该方法不仅利用LASSO进行预测变量的选择，还利用分级贝叶斯方法确定常数系数（CC）还是时变参数（TVP）预测回归在每个样本外预测步骤中都应使用。这种新开发的方法具有模型收缩和CC和TVP预测模型之间自动切换的优点。因此，这可能会产生更准确的原油价格预测。实证结果表明，这种分层收缩模型在各种预测范围内的原油价格预测中，可以优于许多常用的预测基准方法，例如AR，未观察到的成分随机波动率（UCSV）和多元回归模型。