Research on the relationship between global oil prices and economic indicators based on linear regression and ARIMAX models

Abstract

This report aims to analyze the relationship between global oil prices and various economic indexes by using linear regression and ARIMAX models. This study will predict global oil prices accurately and establish a reasonable system for regulating oil prices. The research uses the statistical approach to predict oil prices based on historical data (including independent variables and dependent variable). The study uses monthly average data of WTI crude oil prices from January 2000 to March 2023 and contains the analysis of various economic indicators such as Consumer Price Index (CPI), Personal Consumption Expenditures (PCE), Employment, Population, and Oil Price. The findings indicate that the linear regression model can explain about 40.89% of the variation in log oil price, with significant negative effects of log_PCE, log_EMPLOYMENT, and log_POPULATION, and a significant positive effect of CPI on log_price. However, there exists the probability that some other factors have impact on oil prices. In this study, the author employ the ARIMAX model with ARIMA(4,1,1) errors, which can describe a relatively good fit and small errors in training set measures. Overall, while the linear regression model partially explains the variability in global oil prices, further analysis on residuals is necessary. The study concludes that the ARIMAX model provides a better approach to capture the time-series nature of the data.