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• W3039471369 abstract "We propose a method to conduct uniform inference for the optimal value function, that is, the function that results from optimizing an objective function marginally over one of its arguments. Marginal optimization is not compactly differentiable as a map between the spaces of objective and value functions, which is problematic because standard inference methods for nonlinear maps usually rely on compact differentiability. However, we show that the map from objective function to uniform test statistics applied to the value function - specifically, Kolmogorov-Smirnov or Cramer-von Mises statistics - are directionally differentiable. We establish consistency and weak convergence of nonparametric plug-in estimates of the test statistics. For practical inference, we develop detailed resampling techniques that combine a bootstrap procedure with estimates of the directional derivatives. In addition, we establish local size control of tests which use the resampling procedure. Monte Carlo simulations assess the finite-sample properties of the proposed methods and show accurate empirical size of the procedures. Finally, we apply our methods to the evaluation of a job training program using bounds for the distribution function of treatment effects." @default.
• W3039471369 created "2020-07-10" @default.
• W3039471369 creator A5041435494 @default.
• W3039471369 creator A5068072924 @default.
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• W3039471369 date "2019-11-22" @default.
• W3039471369 modified "2023-09-29" @default.
• W3039471369 title "Uniform inference for value functions" @default.
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• W3039471369 hasPublicationYear "2019" @default.
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