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未观测竞争下的第一价格拍卖非参数识别：密度断点框架AI 解读

We consider nonparametric identification of independent private value first-price auction models, in which the analyst only observes winning bids. Our benchmark model assumes an exogenous number of bidders N. We show that, if the bidders observe N, the resulting discontinuities in the winning bid density can be used to identify the distribution of N. The private value distribution can be nonparametrically identified in a second step. This extends, under testable…

通过筛方法高效估计结构模型AI 解读

We propose a class of sieve-based efficient estimators for structural models (SEES), which approximate the solution using a linear combination of basis functions and impose equilibrium conditions as a penalty to determine the best-fitting coefficients. Our estimators circumvent repeated solution of the structural model, apply to a broad class of models, and are consistent, asymptotically normal, and asymptotically efficient. Moreover, they solve unconstrained optimization…

利用两个次序统计量进行反卷积AI 解读

Economic data are often contaminated by measurement errors and truncated by ranking. This paper shows that the classical measurement error model with independent and additive measurement errors is identified nonparametrically using only two order statistics of repeated measurements. The identification result confirms a hypothesis by Athey and Haile (2002) for a symmetric ascending auction model with unobserved heterogeneity. Extensions allow for heterogeneous measurement…

基于时序与信息集假设的结构模型识别不足AI 解读

We revisit identification based on timing and information set assumptions in structural models, which have been used in the context of production functions, demand equations, and hedonic pricing models (e.g. Olley and Pakes, 1996, Blundell and Bond, 2000). First, we demonstrate a general under-identification problem using these assumptions in a simple version of the Blundell–Bond dynamic panel model. In particular, the basic moment conditions can yield multiple discrete…

利用次序统计量识别拍卖模型AI 解读

Auction data often contain information on only the most competitive bids as opposed to all bids. The usual measurement error approaches to unobserved heterogeneity are inapplicable due to dependence among order statistics. We bridge this gap by providing a set of positive identification results. First, we show that symmetric auctions with discrete unobserved heterogeneity are identifiable using two consecutive order statistics and an instrument. Second, we extend the results…

含不可观测异质性与多重均衡的动态博弈识别AI 解读

This paper provides sufficient conditions for nonparametrically identifying dynamic games with incomplete information, allowing for multiple equilibria and payoff-relevant unobservables. Our identification involves two steps. We first identify the equilibrium conditional choice probabilities and state transitions using the Markov property and four-period data. The first step of our identification relies on eigenvalue-eigenvector decomposition, and thus incurs the same issue…

结构动态 Logit 模型中未观测异质性的充分统计量AI 解读

We study the identification and estimation of structural parameters in dynamic panel data logit models where decisions are forward-looking and the joint distribution of unobserved heterogeneity and observable state variables is nonparametric, i.e., fixed-effects model. We consider models with two endogenous state variables: the lagged decision variable, and the time duration in the last choice. This class of models includes as particular cases important economic applications…

受限随机占优下拍卖中的不可观测异质性AI 解读

We study the identification of first-price auctions with nonseparable unobserved heterogeneity. In particular, we extend Hu et al. (2013) by relaxing the first-order stochastic dominance condition. Instead, we assume restricted stochastic dominance relations among value quantile functions and show that the same relations pass to bid quantile functions. An ordered tree summarizes these relations and provides a total ordering. Relying on the proposed restricted stochastic…

This paper proposes a new methodology for analyzing nonlinear pricing data. We establish identification of the model primitives with a known tariff and characterize the model restrictions on observables. We propose a quantile-based nonparametric estimator that achieves consistency at the parametric rate. We introduce unobserved product heterogeneity with an unknown tariff and show how our identification and estimation results extend. A Monte Carlo study analyzes the…

基于积分分位数的第一价格拍卖模型估计AI 解读

This article considers nonparametric estimation of first-price auction models under the monotonicity restriction on the bidding strategy. Based on an integrated-quantile representation of the first-order condition, we propose a tuning-parameter-free estimator for the valuation quantile function. We establish its cube-root-n consistency and asymptotic distribution under weaker smoothness assumptions than those typically assumed in the empirical literature. If the latter are…