U.S. Imports Price of Goods Forecasting by Customs Basis from China using SARIMA Model

Authors

  • Yanrui Li

DOI:

https://doi.org/10.54097/f7sr4d11

Keywords:

SARIMA; AIC; RMSE; MAPE; Grid search.

Abstract

Changes in the import price of goods will have an influence on the global economy and trade, corporate decision-making, and many other aspects. Therefore, the aim of this research is to forecast the future customs prices of US imports from China. The data comes from the Bureau of Economic Analysis. It can provide an important reference for enterprises, government, and academia to make better decisions. In this paper, due to the strong seasonality, the Seasonal Autoregressive Integrated Moving Average (SARIMA) model is a common technique that is generally used in analyzing and predicting seasonality and stationary time series data. Using the Sarima model to predict U.S. imports of goods from January 1986 to December 2016 and to compare the predicted data with original data. To identify the best model and find out the highest accuracy of the model, this paper utilizes the Grid Search method and calculates Akaike's Information Criterion (AIC), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and adapted MAPE (AMAPE) value as the criterion for selection. The result shows that in the next six months, the amount will still be seasonal, but the overall trend will show small fluctuations and will not change much.

Downloads

Download data is not yet available.

References

Yi Che, Rui Xiao. Import competition, fast-track authority and U.S. policy toward China. Journal of Comparative Economics, 2020, 48(4): 974-996.

U.S. Imports of Goods from China, Mainland, Customs Basis. Guru focus, 2023.

Justin R. Pierce, Peter K. Schott. The surprisingly swift decline of US, manufacturing employment. American Economic Review, 2016, 106(7): 1632–1662.

Clay McManus, Georg Schaur. The effects of import competition on worker health. Journal of International Economics, 2016, 102: 160–172.

David Autor, David Dorn, Gordon Hanson. When work disappears: Manufacturing decline and the falling marriage-market value of men. American Economic Review: Insights, 2019, 1(2): 161–178.

Wayne M. Morrison. China’s economic rise: History, trends, challenges, and implications for the United States. Congressional Research Service Report, 2015.

Shanran Yang, Benye Shi, Fujia Yang. Macroeconomic impact of the Sino–U.S. trade frictions: Based on a two-country, two-sector DSGE model. Research in International Business and Finance, 2023.

Yew-Kwang Ng. Why does the US face greater disadvantages in the trade war with China, China World Economy, 2020, 28(2): 113–122.

Christian Grimme, Robert Lehmann, Marvin Noeller. Forecasting imports with information from abroad, Economic Modelling, 2021, 98: 109-117.

Zhengxin Wang, Yufeng Zhao, Lingyang He. Forecasting the monthly iron ore import of China using a model combining empirical mode decomposition, non-linear autoregressive neural network, and autoregressive integrated moving average. Applied Soft Computing Journal, 2020, 94.

Emmanuel Dave, Albert Leonardo, Marethia Jeanice, Novita Hanafiah, Forecasting Indonesia Exports using a Hybrid Model ARIMA-LSTM. Procedia Computer Science, 2021,179: 480-487.

Wei Fan. Prediction of Monetary Fund Based on ARIMA Model. Procedia Computer Science, 2022, 208:277-285.

Lai Huihui. Forecast of passenger vehicle consumption tax based on Prophet model. Research on tax economy, 2020, 25(1): 34-39.

Astuti S W, Jamaludin. Forecasting Surabaya Jakarta train passengers with SARIMA model. IOP Conference Series Materials Science and Engineering, 2018, 407(1).

S.O. Adams, B. Mustapha, A.I. Alumbugu, Seasonal autoregressive integrated moving average (SARIMA) model for the analysis of frequency of monthly rainfall in Osun state, Nigeria. Physical Science International Journal, 2019, 22(4): 1–9.

Vishal Kushwaha, Naran M. Pindoriya. A SARIMA-RVFL hybrid model assisted by wavelet decomposition for very short-term solar PV power generation forecast. Renew Energy, 2019, 140:124–139.

Yanming Yang, Haiyan Zheng, Ruili Zhang. Prediction and analysis of aircraft failure rate based on SARIMA model. 2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA), 2017.

Hmeda Musbah, Mo El-Hawary. Sarima model forecasting of short-term electrical load data augmented by fast Fourier transform seasonality detection. In: 2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE), 2019.

Box G E P, Jenkins G M. Time series analysis forecasting and control. Holden Day, 1976.

Nobre F F, et al. Dynamic linear model and SARIMA: a comparison of their forecasting performance in epidemiology. Statistics in Medicine, 2001, 20(20): 3051–3069.

Downloads

Published

29-03-2024

How to Cite

Li, Y. (2024). U.S. Imports Price of Goods Forecasting by Customs Basis from China using SARIMA Model. Highlights in Science, Engineering and Technology, 88, 1008-1015. https://doi.org/10.54097/f7sr4d11