SAM平衡的SG-RAS與SG-CE方法

摘要： 隨機(jī)cross entropy(Robinson et al.,2001)方法是目前用于平衡社會核算矩陣的最主流方法,本文綜合其與GRAS(Lenzen et al.,2007)方法的思想,擴(kuò)展提出了SG-CE(stochastic generalized CE)與SG-RAS方法,避免了原始CE方法在平衡SAM時需要對負(fù)值元素進(jìn)行預(yù)處理的步驟,試圖改進(jìn)由此引致的缺陷并提高平衡SAM質(zhì)量。在此基礎(chǔ)上,本文運(yùn)用SG-RAS、SG-CE、CE以及國內(nèi)常用的余量法對中國2007年SAM進(jìn)行了平衡,實(shí)證結(jié)果的對比表明:在平衡系數(shù)SAM時,SG-RAS優(yōu)勢非常明顯;而平衡流量SAM時,除SG-RAS之外,余量法也具較好表現(xiàn);但無論是平衡系數(shù)還是流量SAM,SG-CE的效果只能說差強(qiáng)人意,原始CE方法更差。
Cross entropy(CE) method is the most popular technique for balancing SAMs in recent years.This paper develops the stochastic generalized CE/RAS(SG-CE/SG-RAS) method by extending present stochastic CE(Robinson et al.,2001) and GRAS(Lenzen et al.,2007),to directly deal with the initial unbalanced matrix including negative elements.Noted that the CE method need to detect any negative flows first and then net them out of their respective symmetric cells,which would change the structure of matrix and the sigh of some elements in the balancing procedure,but neither SG-CE nor SG-RAS wouldn't.The empirical analysis by utilizing four techniques(SG-RAS,SG-CE,CE,termwise) to balance 2007's macro and micro SAMs of China shows that: SG-RAS perform best for estimating coefficient SAM;SG-RAS and termwise methods are both good enough in transaction SAM estimating;however,SG-CE doesn't performs as good as expected when estimating coefficient SAM and transaction SAM.The Cross entropy method performs poorer.