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来源类型Working Paper
规范类型报告
DOI10.3386/w17557
来源IDWorking Paper 17557
Identifying Demand with Multidimensional Unobservables: A Random Functions Approach
Jeremy T. Fox; Amit Gandhi
发表日期2011-11-03
出版年2011
语种英语
摘要We explore the identification of nonseparable models without relying on the property that the model can be inverted in the econometric unobservables. In particular, we allow for infinite dimensional unobservables. In the context of a demand system, this allows each product to have multiple unobservables. We identify the distribution of demand both unconditional and conditional on market observables, which allows us to identify several quantities of economic interest such as the (conditional and unconditional) distributions of elasticities and the distribution of price effects following a merger. Our approach is based on a significant generalization of the linear in random coefficients model that only restricts the random functions to be analytic in the endogenous variables, which is satisfied by several standard demand models used in practice. We assume an (unknown) countable support for the the distribution of the infinite dimensional unobservables.
主题Econometrics ; Industrial Organization
URLhttps://www.nber.org/papers/w17557
来源智库National Bureau of Economic Research (United States)
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资源类型智库出版物
条目标识符http://119.78.100.153/handle/2XGU8XDN/575231
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Jeremy T. Fox,Amit Gandhi. Identifying Demand with Multidimensional Unobservables: A Random Functions Approach. 2011.
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