A Novel Grey Wave Method for Predicting Total Chinese Trade Volume

Kedong Yin, Danning Lu and Xuemei Li
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Kedong Yin: School of Economics, Ocean University of China, Qingdao 266100, China
Danning Lu: School of Economics, Ocean University of China, Qingdao 266100, China
Xuemei Li: School of Economics, Ocean University of China, Qingdao 266100, China

Sustainability, 2017, vol. 9, issue 12, 1-16

Abstract: The total trade volume of a country is an important way of appraising its international trade situation. A prediction based on trade volume will help enterprises arrange production efficiently and promote the sustainability of the international trade. Because the total Chinese trade volume fluctuates over time, this paper proposes a Grey wave forecasting model with a Hodrick–Prescott filter (HP filter) to forecast it. This novel model first parses time series into long-term trend and short-term cycle. Second, the model uses a general GM (1,1) to predict the trend term and the Grey wave forecasting model to predict the cycle term. Empirical analysis shows that the improved Grey wave prediction method provides a much more accurate forecast than the basic Grey wave prediction method, achieving better prediction results than autoregressive moving average model (ARMA).

Keywords: Grey wave forecasting; HP filter; trade sustainability; tendency component (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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