Monotonicity-Constrained Nonparametric Estimation and Inference for First-Price Auctions

Abstract

We propose a new nonparametric estimator for first-price auctions with independent private values that imposes the monotonicity constraint on the estimated inverse bidding strategy. We show that our estimator has a smaller asymptotic variance than that of Guerre, Perrigne and Vuong's (2000) estimator. In addition to establishing pointwise asymptotic normality of our estimator, we provide a bootstrap-based approach to constructing uniform confidence bands for the density function of latent valuations.

References in corpus (5)

• Improving Point and Interval Estimates of Monotone Functions by Rearrangement
• Gaussian approximation of suprema of empirical processes
• Anti-concentration and honest, adaptive confidence bands
• Nonparametric regression with nonparametrically generated covariates
• Inference for First-Price Auctions with Guerre, Perrigne, and Vuong's Estimator