| 来源类型 | Working Paper
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| 规范类型 | 报告
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| DOI | 10.3386/w17557
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| 来源ID | Working Paper 17557
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| Identifying Demand with Multidimensional Unobservables: A Random Functions Approach |
| Jeremy T. Fox; Amit Gandhi
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| 发表日期 | 2011-11-03
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| 出版年 | 2011
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| 语种 | 英语
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| 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
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| URL | https://www.nber.org/papers/w17557
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| 来源智库 | National Bureau of Economic Research (United States)
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| 引用统计 |
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| 资源类型 | 智库出版物
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| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/575231
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推荐引用方式
GB/T 7714 |
Jeremy T. Fox,Amit Gandhi. Identifying Demand with Multidimensional Unobservables: A Random Functions Approach. 2011.
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文件名：
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w17557.pdf
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格式：
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Adobe PDF
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