社會核算矩陣平衡方法研究

摘要： 本文針對雙比例尺度(RAS)、交叉熵(CE)等方法在平衡社會核算矩陣(SAM)中僅從技術(shù)層面機(jī)械地進(jìn)行平衡化處理致使先驗信息損失的問題,提出了加權(quán)離差熵平方期望最小化方法;并以先驗信息為基礎(chǔ),構(gòu)造了初始加權(quán)矩陣和可行加權(quán)矩陣。同時,本文以中國2007年的非平衡SAM為例,對比研究RAS、CE和加權(quán)離差熵平方期望最小化三種方法對其進(jìn)行平衡化處理的實(shí)際效果。結(jié)果表明:RAS方法得到的結(jié)果偏差相對較大,而CE方法和加權(quán)離差熵平方期望最小化方法得到的結(jié)果相對較精準(zhǔn);此外,加權(quán)離差熵平方期望最小化方法能夠有效利用先驗信息,避免有效信息的無謂損失。
Considering the defects of the RAS and Cross-Entropy(CE) approaches that losing the priori information when they are applied for the balance of the Social Accounting Matrix(SAM),this paper proposes the weighted approach which is based on minimizing the expectation of the deviation entropy square.And it constructs the weighting matrix in accordance with the degree of the prior information.Meanwhile,this paper takes the Chinese unbalanced SAM in 2007 as an example,and compares the real effects of balancing among RAS approach,CE approach and weighted approach based on minimizing the expectation of the deviation entropy square.And the results show that: RAS approach gets the results with a larger deviation,while the results produced by CE approach and weighted approach based on minimizing the expectation of the deviation entropy square are more accurate.Furthermore,weighted approach based on minimizing the expectation of the deviation entropy square can make flexible and effective use of the prior information and avoiding this deadweight loss of effective information,but RAS and CE approaches do not have this advantage.