Nonparametric Estimation of Utility Functions

通常假设消费者的需求函数是由某种效用函数的最大化产生的。效用函数的详细知识对于预测不同环境下的消费者需求和评估政策变化对福利的影响是必要的。这个项目的目的是开发非参数方法来估计消费者需求观察的效用函数。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规格错误。估计非参数需求函数的计算复杂性需要使用超级计算机，该合同将为开发和测试非参数估计方法提供超级计算机。

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.