Nguyen, T., Nguyen, H., Lee, J., Wang, Y., & Tsai, C. (2023). The Consumer Price Index Prediction Using Machine Learning Approaches: Evidence from the United States. Heliyon, 9, e20730.
https://doi.org/10.1016/j.heliyon.2023.e20730

has been cited by the following article:

• TITLE: Forecasting CPI and PPI Using Commodity Prices and Crude Oil Prices: A Comparison of China and United States

  AUTHORS: Yitong He, Linru Yang, Lei Chen

  KEYWORDS: CPI Forecasting, PPI Forecasting, Commodity Prices, Crude Oil Prices, China, United States

  JOURNAL NAME: Modern Economy, Vol.15 No.11, November 6, 2024

  ABSTRACT: This paper examines the forecasting of the Consumer Price Index (CPI) and Producer Price Index (PPI) using commodity price index and crude oil price, focusing on a comparison between China and the United States. Using an autoregressive moving average model with exogenous variables and a rolling window extrapolation method, we analyze data from July 2018 to December 2023. The results show that CPI forecasting consistently outperforms PPI forecasting in both nations. Additionally, both commodity price index and crude oil price enhance CPI and PPI forecasts. Crude oil futures prices significantly improve CPI predictions, while commodity price indices benefit PPI forecasts. Furthermore, WTI crude oil futures outperform INE crude oil futures in forecasting CPI for both China and the United States.