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显示偏好分析的频率学派方法AI 解读

This paper develops a framework to study the statistical power of revealed-preference tests. With randomly sampled budgets and mild smoothness of demand, statistical learning implies that any model consistent with the data must approximate true choice behaviour. We interpret this result as follows: passing a revealed-preference test is informative only to the extent that the data are sufficiently rich to rule out economically meaningful departures from the maintained model.…

个体异质性下的消费者福利AI 解读

We propose a nonparametric method for estimating the distribution of consumer welfare from cross-sectional data with no restrictions on individual preferences. First demonstrating that moments of demand identify the curvature of the expenditure function, we use these moments to approximate money-metric welfare measures. Our approach captures both nonhomotheticity and heterogeneity in preferences in the behavioral responses to price changes. We apply our method to US…