This paper proposes alternative methodologies for oil price forecasting using mixed-frequency data and a textual sentiment indicator. The latter variable was extracted from oil market reports issued by the Energy Information Administration. We used the root mean square error (RMSE) to evaluate the forecasting accuracy of the econometric models. Compared with other econometric models, the mixed data sampling (MIDAS) model with high-frequency financial indicators and the sentiment index as explanatory variables performs better for forecasting crude oil prices.

中文翻译：

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预测油价：新方法

本文提出了使用混合频率数据和文本情绪指标进行油价预测的替代方法。后一个变量是从能源信息署发布的石油市场报告中提取的。我们使用均方根误差 (RMSE) 来评估计量经济模型的预测准确性。与其他计量经济模型相比，以高频财务指标和情绪指数为解释变量的混合数据抽样（MIDAS）模型在预测原油价格方面表现更好。