偏好技术:最小信息下消费支出与增加值的关联

Esteban Fernández-Vázquez, Mònica Serrano
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引用次数: 1

摘要

将支出调查所得的消费数据与本组织表格所载资料结合起来,是结构变化分析的关键步骤。Herrendorf等人(2013)在将模型与任何多部门一般均衡模型中的数据联系起来时,将注意力集中在对家庭和生产双方应用一致的商品定义上——即效用和生产函数的估计。这些分析的出发点基本上是将家庭消费信息与IO表中出现的最终需求向量(或矩阵)联系起来,然后方便地对其进行修改,以产生感兴趣的乘数。这个过程需要构建一个一致性或桥梁矩阵,使这种联系成为可能,因为有几个问题影响这两个数据源的组合:消费调查和IO表之间的价格估值差异,税收和利润的影响或这两个框架之间不同的产品分类使这种组合成为研究人员的挑战。在本文中,我们以双重目的探讨这一挑战:(i)调查在所谓的总需求矩阵或影响分析方面,我们的消费数据在住户调查和IO表之间的“好”或“坏”调和对我们的结果有多重要;(ii)在两种数据结构之间提出一种调解技术,这种技术只使用最少的信息,在没有详细数据的情况下提供一种系统的方法来调和它们。该技术基于熵计量经济学,它允许对估计的桥矩阵进行统计推断。这两个研究目标都是通过数值模拟和应用到一个现实世界的情况下说明。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Technology of the Preferences: Linking Consumption Expenditures to Value Added with Minimal Information
The combination of consumption data from expenditure surveys with information contained in IO tables is a crucial step to structural change analysis. Herrendorf et al. (2013) focus the attention on applying a consistent definition of commodities on both the household and production sides — i.e. estimation of utility and production functions — when connecting models with data in any multisector general equilibrium model. The point of departure of these analyses consist, basically, on connecting the information on consumption made by households with the final demand vector (or matrix) present in the IO tables, which is then conveniently modified to produce the multipliers of interest. This process requires the construction of a concordance or bridge matrix to make this connection possible, since several issues affect the combination of these two data sources: differences in price valuation between consumption surveys and IO tables, the influence of taxes and margins or the different product classifications between these two frameworks make this combination a challenge for the researcher. In this paper we explore this challenge with a twofold purpose: (i) to investigate how important a “good” or “bad” conciliation of our consumption data between household surveys and IO tables affect our results in terms of the so-called total requirement matrix or impact analysis; and (ii) to propose a conciliation technique between both data structure, which using only minimal information provides a systematic way or reconciling them if detailed data are not at hand. This technique is based on entropy econometrics and it allows making statistical inference on the bridge matrix estimated. Both research objectives are illustrated by means of numerical simulation and by its application to a real-world case.
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