Nonparametric Estimation of Utility Functions

效用函数的非参数估计

• 批准号: 8713532
• 负责人: Rosa Matzkin
• 金额: --
• 依托单位: Yale University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1987
• 资助国家: 美国
• 起止时间: 1987-10-15 至 1988-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8713532&HistoricalAwards=false
• 关键词: Nonparametric; Estimation; Utility; Functions

It is commonly assumed that consumers' demand functions are generated by the maximization of some utility function. Detailed knowledge of utility functions is necessary for the prediction of consumer demand under different environments and for the assessment of the welfare implications of policy changes. The purpose of this project is to develop nonparametric methods for estimating utility functions from consumer-demand observations. The methods are nonparametric because they do not require the specification of a specific functional form. This is important because this approach can eliminate misspecification errors leading to faulty predictions. The computational complexity of estimating nonparametric demand functions requires the use of a supercomputer, and this award will provide access to a supercomputer for developing and testing nonparametric estimation methods.

通常假设消费者的需求函数是通过某些效用函数的最大化而产生的。 详细了解效用函数对于预测不同环境下的消费者需求以及评估政策变化的福利影响是必要的。 该项目的目的是开发非参数方法，根据消费者需求观察来估计效用函数。 这些方法是非参数的，因为它们不需要指定特定的函数形式。 这很重要，因为这种方法可以消除导致错误预测的错误指定错误。 估计非参数需求函数的计算复杂性需要使用超级计算机，该奖项将提供使用超级计算机来开发和测试非参数估计方法的机会。