Forecasting Oil Consumption: The Statistical Review of World Energy Meets Machine Learning

2026-02-02

AI Summary

This study addresses the challenge of improving the accuracy of global and regional oil consumption forecasts by identifying a small set of dominant countries as key drivers. The authors construct a high-dimensional concentration matrix and employ LASSO combined with the One Covariate Multiple Testing (OCMT) procedure to select countries with statistically significant influence. They further propose a novel mechanism for identifying dominant nations based on column-norm ranking and a sequential ratio criterion, augmented by economic constraints to eliminate spurious correlations. The identified dominant countries—such as the United States as the global hub, and France and Japan as robust regional centers in Europe and Asia, respectively—are incorporated into a common factor forecasting model. Empirical results demonstrate that this framework substantially outperforms conventional autoregressive and country-specific LASSO models, with particularly notable gains in forecast accuracy during periods of heightened global volatility.

Technology Category

• Search and Optimization: Algorithm Configuration
• Machine Learning: Large Multimodal Models (LMMs)
• Intelligent Robots: State Estimation

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

This paper studies whether a small set of dominant countries can account for most of the dynamics of regional oil demand and improve forecasting performance. We focus on dominant drivers within the OECD and a broad GVAR sample covering over 90\% of world GDP. Our approach identifies dominant drivers from a high-dimensional concentration matrix estimated row by row using two complementary variable-selection methods, LASSO and the one-covariate-at-a-time multiple testing (OCMT) procedure. Dominant countries are selected by ordering the columns of the concentration matrix by their norms and applying a criterion based on consecutive norm ratios, combined with economically motivated restrictions to rule out pseudo-dominance. The United States emerges as a global dominant driver, while France and Japan act as robust regional hubs representing European and Asian components, respectively. Including these dominant drivers as regressors for all countries yields statistically significant forecast gains over autoregressive benchmarks and country-specific LASSO models, particularly during periods of heightened global volatility. The proposed framework is flexible and can be applied to other macroeconomic and energy variables with network structure or spatial dependence.

Problem

• oil consumption forecasting
• dominant countries
• regional oil demand
• forecasting performance
• energy economics

Innovation

• dominant drivers
• concentration matrix
• LASSO
• OCMT
• forecasting oil consumption