Abstract
After President Trump came to power, in order to change the “imbalance” between China and US trade, he launched a trade war with China, which led to increase uncertainty in China-US trade and increased export volatility. Based on R language environment, this paper compares the advantages and disadvantages of seasonal ARIMA (p, d, q) model and double-index ETS (A, N, A) model in short-term forecast of China’s total export value to the United States. Then, the double-index ETS (A, N, A) model is selected to predict the trend of China’s export trade to the United States in the months of 2009-2020. The forecast results show that China’s export to the United States has seasonal characteristics. The export fluctuation is smaller than that in 2018, but the total value of exports has decreased significantly. Finally, some suggestions are put forward.
Highlights
After President Trump came to power, in order to change the “imbalance” between China and US trade, he launched a trade war with China, which led to increase uncertainty in China-US trade and increased export volatility
Against the background of the trade war between China and the United States, some scholars conducted studies on the prediction of the development trend of sino-American trade through CGE model, game model and tv-stvar model, and the results showed that bilateral trade, GDP growth and social welfare would be affected
The innovation of this paper is that based on R language environment, seasonal ARIMA (p, d, q) model and double index ETS (A, N, A) model are applied in the field of international trade to predict the short-term trend of sino-American trade value, so as to study the impact of sino-American trade war and provide guidance for dealing with trade war
Summary
Against the background of the trade war between China and the United States, some scholars conducted studies on the prediction of the development trend of sino-American trade through CGE model, game model and tv-stvar model, and the results showed that bilateral trade, GDP growth and social welfare would be affected. The innovation of this paper is that based on R language environment, seasonal ARIMA (p, d, q) model and double index ETS (A, N, A) model are applied in the field of international trade to predict the short-term trend of sino-American trade value, so as to study the impact of sino-American trade war and provide guidance for dealing with trade war. The idea of exponential smoothing method is derived from the improvement of moving average prediction method, which comprehensively uses adjacent values, overall trend and seasonality to conduct prediction analysis, but gives more weight to adjacent values This kind of model is proved to be good for short-term prediction in practice. June 2019 for time series analysis, model and model mainly uses the short-term prediction of China’s export trade value to determine whether the total value of China’s export to the United States will decrease significantly or increase after the United.
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