GRAS增强方法及其应用OA北大核心CHSSCDCSSCICSTPCD

The GRAS method remains one of the most widely used approaches for updating input-output tables or social ac-counting matrices,but there is still room for improvement.In this context,this paper constructs the TGRAS method,and analyzes and tests the update accuracy of GRAS method and TGRAS method based on the update results of input-output table.The results go as the following:(1)After the GRAS method is applied,the absolute percentage error(APE)is larger for sectors with smaller to-tal output(or total input),revealing an asymmetry in the update results.(2)By incorporating additional information for sectors with smaller total output(or total input),the TGRAS method produces lower values for the information loss index,information relevance index,standardized total percentage error,and deviation distance index compared to the GRAS method.This effectively mitigates the asymmetry in the GRAS method's update results,resulting in a significant higher update accuracy for the TGRAS method.(3)Even when the long short-term memory network model is employed to incorporate additional information for sectors with smaller total output(or total input),the TGRAS method shows only a slight increase in the information relevance index,while still main-taining superior update accuracy compared to the GRAS method.