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.
KeywordsForecastingEnergy ConsumptionLeading IndicatorsARDLX ModelsTransportation Indices
Alquist, R., & Kilian, L. (2010). What Do We Learn from the Price of Crude Oil? Journal of Economic Literature, 48, 365-403.
Baimpos, G., & Kyriazi, F. (2025). Nonlinear GDP Forecasting: A Threshold-Ardlx Approach with Leading Macroeconomic Indicators. Theoretical Economics Letters, 15, 446-460. https://doi.org/10.4236/tel.2025.152024
Baumeister, C., & Kilian, L. (2015). Forecasting the Real Price of Oil in a Changing World: A Forecast Combination Approach. Journal of Business & Economic Statistics, 33, 338-351. https://doi.org/10.1080/07350015.2014.949342
Baumeister, C., & Peersman, G. (2013). Time-Varying Effects of Oil Supply Shocks on the US Economy. American Economic Journal: Macroeconomics, 5, 1-28. https://doi.org/10.1257/mac.5.4.1
Baumeister, C., Korobilis, D., & Lee, T. K. (2020). Energy Markets and Global Economic Conditions. The Review of Economics and Statistics, 104, 828-844. https://doi.org/10.1162/rest_a_00977
Billé, A. G., Gianfreda, A., Del Grosso, F., & Ravazzolo, F. (2023). Forecasting Electricity Prices with Expert, Linear, and Nonlinear Models. International Journal of Forecasting, 39, 570-586. https://doi.org/10.1016/j.ijforecast.2022.01.003
El Maghraoui, A., Ledmaoui, Y., Laayati, O., El Hadraoui, H., & Chebak, A. (2022). Smart Energy Management: A Comparative Study of Energy Consumption Forecasting Algorithms for an Experimental Open-Pit Mine. Energies, 15, Article 4569. https://doi.org/10.3390/en15134569
Emami Javanmard, M., Tang, Y., & Martínez-Hernández, J. A. (2024). Forecasting Air Transportation Demand and Its Impacts on Energy Consumption and Emission. Applied Energy, 364, Article ID: 123031. https://doi.org/10.1016/j.apenergy.2024.123031
Ferrari, D., Ravazzolo, F., & Vespignani, J. (2019). Forecasting Energy Commodity Prices: A Large Global Dataset Sparse Approach. SSRN Electronic Journal . https://doi.org/10.2139/ssrn.3507158
Gao, L., Kim, H., & Saba, R. (2014). How Do Oil Price Shocks Affect Consumer Prices? Energy Economics, 45, 313-323. https://doi.org/10.1016/j.eneco.2014.08.001
Graham, D. J., & Glaister, S. (2004). Road Traffic Demand Elasticity Estimates: A Review. Transport Reviews, 24, 261-274. https://doi.org/10.1080/0144164032000101193
Guerard, J. B., Thomakos, D., Kyriazi, F., & Beheshti, B. (2024). The Development and Evolution of Mean-Variance Efficient Portfolios in the US and Japan: 30 Years after the Markowitz and Ziemba Applications. Annals of Operations Research . https://doi.org/10.1007/s10479-024-06138-7
Guerard, J., Thomakos, D., & Kyriazi, F. (2020). Automatic Time Series Modeling and Forecasting: A Replication Case Study of Forecasting Real GDP, the Unemployment Rate and the Impact of Leading Economic Indicators. Cogent Economics & Finance, 8, Article ID: 1759483. https://doi.org/10.1080/23322039.2020.1759483
Guerard, J., Thomakos, D., Kyriazi, F., & Mamais, K. (2023). On the Predictability of the DJIA and S&P500 Indexes. Wilmott, No. 129 , 1-84. https://doi.org/10.54946/wilm.12006
Hamilton, J. D. (1983). Oil and the Macroeconomy since World War II. Journal of Political Economy, 91, 228-248. https://doi.org/10.1086/261140
Huang, B., Hwang, M. J., & Peng, H. (2005). The Asymmetry of the Impact of Oil Price Shocks on Economic Activities: An Application of the Multivariate Threshold Model. Energy Economics , 27, 455-476. https://doi.org/10.1016/j.eneco.2005.03.001
Karamperidis, S., Melas, K. D., & Michail, N. A. (2024). Econometric Insights into LNG Carrier Port Congestion and Energy Inflation: A Data-Driven Approach. Commodities, 3 , 462-471. https://doi.org/10.3390/commodities3040026
Kaufmann, R. K. (2023). Energy Price Volatility Affects Decisions to Purchase Energy Using Capital: Motor Vehicles. Energy Economics, 126, Article ID: 106915. https://doi.org/10.1016/j.eneco.2023.106915
Kilian, L. (2009). Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market. American Economic Review, 99, 1053-1069. https://doi.org/10.1257/aer.99.3.1053
Kümmel, R. (1982). The Impact of Energy on Industrial Growth. Energy, 7, 189-203. https://doi.org/10.1016/0360-5442(82)90044-5
Kyriazi, F. (2024). The Prescriptive Nature of Market Timing and Predictive Portfolios. IMA Journal of Management Mathematics, 36, 323-338. https://doi.org/10.1093/imaman/dpae027
Kyriazi, F., & Thomakos, D. D. (2020a). Foreign Exchange Rate Predictability: Seek and Ye Shall Find It. In J. B. Guerard, & W. T. Ziemba (Eds.), Handbook of Applied Investment Research (pp. 511-556). World Scientific. https://doi.org/10.1142/9789811222634_0020
Kyriazi, F., & Thomakos, D. D. (2020b). Distance-based Nearest Neighbour Forecasting with Application to Exchange Rate Predictability. IMA Journal of Management Mathematics, 31, 469-490. https://doi.org/10.1093/imaman/dpz016
Li, S., Timmins, C., & von Haefen, R. H. (2009). How Do Gasoline Prices Affect Fleet Fuel Economy? American Economic Journal: Economic Policy, 1, 113-137. https://doi.org/10.1257/pol.1.2.113
Lin, B., & Zhu, J. (2020). The Role of Air Transportation in Energy Consumption and Emissions in China. Energy Sources, Part B: Economics, Planning, and Policy, 15, 1-9.
Notteboom, T., & Rodrigue, J. P. (2008). The Future of Containerization: Perspectives from Maritime and Inland Freight Distribution. GeoJournal , 76, 457-465.
Prokopos, G., & Kyriazi, F. (2025). Adaptive Learning in Short Time Series. Theoretical Economics Letters, 15, 674-688. https://doi.org/10.4236/tel.2025.153036
Punzi, M. T. (2019). The Impact of Energy Price Uncertainty on Macroeconomic Variables. Energy Policy, 129, 1306-1319. https://doi.org/10.1016/j.enpol.2019.03.015
Small, K. A., & Van Dender, K. (2007). Fuel Efficiency and Motor Vehicle Travel: The Declining Rebound Effect. The Energy Journal, 28, 25-52. https://doi.org/10.5547/issn0195-6574-ej-vol28-no1-2
Smith, K. R., Dutta, K., Chengappa, C., Gusain, P. P. S., Berrueta, O. M. a. V., Edwards, R. et al. (2007). Monitoring and Evaluation of Improved Biomass Cookstove Programs for Indoor Air Quality and Stove Performance: Conclusions from the Household Energy and Health Project. Energy for Sustainable Development, 11, 5-18. https://doi.org/10.1016/s0973-0826(08)60396-8
Stratton, A. (1979). Energy Forecasting. Omega, 7, 493-502. https://doi.org/10.1016/0305-0483(79)90067-7
Suganthi, L., & Samuel, A. A. (2012). Energy Models for Demand Forecasting—A Review. Renewable and Sustainable Energy Reviews, 16, 1223-1240. https://doi.org/10.1016/j.rser.2011.08.014
Tiwari, A. K., Aikins Abakah, E. J., Trabelsi, N., & Wohar, M. (2024). Do Shipping Freight Markets Impact Commodity Markets? International Review of Economics & Finance, 91, 986-1014. https://doi.org/10.1016/j.iref.2024.01.066
Valadkhani, A. (2014). Dynamic Effects of Rising Oil Prices on Consumer Energy Prices in Canada and the United States: Evidence from the Last Half a Century. Energy Economics, 45, 33-44. https://doi.org/10.1016/j.eneco.2014.06.015
Wadud, Z. (2015). Imperfect Reversibility of Air Transport Demand: Effects of Air Fare, Fuel Prices and Price Transmission. Transportation Research Part A: Policy and Pra ctice, 72, 16-26. https://doi.org/10.1016/j.tra.2014.11.005
Wei, N., Li, C., Peng, X., Zeng, F., & Lu, X. (2019). Conventional Models and Artificial Intelligence-Based Models for Energy Consumption Forecasting: A Review. Journal of Petroleum Science and Engineering, 181, Article ID: 106187. https://doi.org/10.1016/j.petrol.2019.106187
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
Zachariadis, T. (2007). Exploring the Relationship between Energy Use and Economic Growth with Bivariate Models: New Evidence from G-7 Countries. Energy Economics, 29, 1233-1253. https://doi.org/10.1016/j.eneco.2007.05.001