An Application of Grey Theory to Global Trade Predictions Based on Airport Cargo Traffic

Abstract

t is vital for producers and service providers to understand the possible development of future global trade. Throughputs at gateways directly reveal the status of trade. This study constructs prediction models for yearly global export trade values, using Grey theory, by means of an analysis of airport cargo traffic, from 2000 to 2009. Two steps were involved in the construction of a GM(1,N) Grey prediction model. Firstly, using Grey relational analysis, candidates were selected from world's leading twenty-one airports that have complete data for cargo throughputs. Paris CDG, Louisville and Frankfurt airports were then chosen, as they had higher Grey correlations with global export trade value. Secondly, a GM(1,4) model was produced, to forecast trade values, based on the cargo traffic at these three airports. The evaluation results revealed that this model had good forecasting abilities and was better than that of GM(1,1).