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技能形成模型中的归一化与误设AI 解读

Abstract An important class of structural models studies the determinants of skill formation and the optimal timing of interventions. In this article, I provide new identification results for these models and investigate the effects of seemingly innocuous scale and location restrictions on parameters of interest. To do so, I first characterize the identified set of all parameters without these additional restrictions and show that important policy-relevant parameters are…

升价拍卖的识别及其数字版权管理应用AI 解读

This study provides new identification and estimation results for ascending (traditional English or online) auctions with unobserved auction‐level heterogeneity and an unknown number of bidders. When the seller's reserve price and two order statistics of bids are observed, we derive conditions under which the distributions of buyer valuations, unobserved heterogeneity, and number of participants are point identified. We also derive conditions for point identification in…

具有交互固定效应的非参数面板数据模型AI 解读

This article studies non-parametric panel data models with multidimensional, unobserved individual effects when the number of time periods is fixed. I focus on models where the unobservables have a factor structure and enter an unknown structural function non-additively. The setup allows the individual effects to impact outcomes differently in different time periods and it allows for heterogeneous marginal effects. I provide sufficient conditions for point identification of…

一致置信带的刻画与最优性AI 解读

This paper studies optimal uniform confidence bands for functions g(x,β0), where β0 is an unknown parameter vector. We provide a simple characterization of a general class of taut 1−α uniform confidence bands, allowing for both nonlinear functions and nonparametrically estimated functions. Specifically, we show that all taut bands can be obtained from projections on confidence sets for β0 and we characterize the class of sets which yield taut bands. Using these results, we…

非参数工具变量模型中的完备性与一致性AI 解读

This paper provides positive testability results for the identification condition in a nonparametric instrumental variable model, known as completeness, and it links the outcome of the test to properties of an estimator of the structural function. In particular, I show that the data can provide empirical evidence in favor of both an arbitrarily small identified set as well as an arbitrarily small asymptotic bias of the estimator. This is the case for a large class of…

非参数工具变量估计中的识别与形状约束AI 解读

This paper is concerned with inference about an unidentified linear functional, L(g), where g satisfies Y=g(X)+U; E(U|W)=0. In much applied research, X and W are discrete, and W has fewer points of support than X. Consequently, L(g) is not identified nonparametrically and can have any value in (−∞,∞). This paper uses shape restrictions, such as monotonicity or convexity, to achieve interval identification of L(g). The paper shows that under shape restrictions, L(g) is…

多市场差异化产品需求模型的渐近理论AI 解读

This paper develops asymptotic theory for differentiated product demand models with a large number of markets T. It takes into account that the predicted market shares are approximated by Monte Carlo integration with R draws and that the observed market shares are approximated from a sample of N consumers. The estimated parameters are T consistent and asymptotically normal as long as R and N grow fast enough relative to T. Both approximations yield additional bias and…