摘要翻译：
常用的生产函数和加价估计方法假定企业的产出量可以作为数据来观察，但典型的数据集只包含收入，而不包含产出量。在不完全竞争条件下，当企业面对一般的非参数需求函数时，我们从收入数据中考察了生产函数和加价的非参数识别。在标准假设下，我们提供了各种企业层面对象的构造性非参数识别：总生产函数、全要素生产率、相对于边际成本的价格加价、产出价格、产出数量、一个需求系统和一个有代表性的消费者效用函数。
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英文标题：
《Nonparametric Identification of Production Function, Total Factor
Productivity, and Markup from Revenue Data》
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作者：
Hiroyuki Kasahara and Yoichi Sugita
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最新提交年份：
2020
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分类信息：

一级分类：Economics 经济学
二级分类：Econometrics 计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
Commonly used methods of production function and markup estimation assume that a firm\'s output quantity can be observed as data, but typical datasets contain only revenue, not output quantity. We examine the nonparametric identification of production function and markup from revenue data when a firm faces a general nonparametri demand function under imperfect competition. Under standard assumptions, we provide the constructive nonparametric identification of various firm-level objects: gross production function, total factor productivity, price markups over marginal costs, output prices, output quantities, a demand system, and a representative consumer\'s utility function.
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