Xu, Y., Liu, T., Fang, Q., Du, P., & Wang, J. (2025). Crude Oil Price Forecasting with Multivariate Selection, Machine Learning, and a Nonlinear Combination Strategy. Engineering Applications of Artificial Intelligence, 139, Article ID: 109510.
https://doi.org/10.1016/j.engappai.2024.109510

has been cited by the following article:

• TITLE: An ARDLX-Based Multi-Method Analysis of Energy CPI with Composite Transportation Indices

  AUTHORS: Evangelos Kastas, Vasileios Voudouris, Foteini Kyriazi

  KEYWORDS: Forecasting, Energy Consumption, Leading Indicators, ARDLX Models, Transportation Indices

  JOURNAL NAME: Theoretical Economics Letters, Vol.15 No.4, August 6, 2025

  ABSTRACT: In this paper, we construct and evaluate macroeconomic and transportation-based indices to assess their predictive power in forecasting the Energy Consumer Price Index (CPI). We introduce three novel indices that incorporate global crude oil prices, industrial production, deep-sea freight, air transportation, and motor vehicle equipment. To assess their forecasting ability, we compare their performance against AR(1) and AR(2) benchmark models using rolling window approaches and two estimation methods: ordinary least squares and quantile regression. The results demonstrate that our proposed indices consistently outperform the benchmarks across most model specifications and window lengths, with transportation-related indicators showing particularly strong and robust predictive performance for future energy price trends.