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集合值预测与模型误设下的信息推断AI 解读

This paper proposes an information-based inference method for partially identified parameters in incomplete models that is valid both when the model is correctly specified and when it is misspecified. Key features of the method are: (i) it is based on minimizing a suitably defined Kullback-Leibler information criterion that accounts for incompleteness of the model and delivers a non-empty pseudotrue set; (ii) it is computationally tractable; (iii) its implementation is the…

Summary This paper studies nonparametric identification in market-level demand models for differentiated products with heterogeneous consumers. We consider a general class of models that allows for the individual-specific coefficients to vary continuously across the population and give conditions under which the density of these coefficients, and hence also functionals such as the fractions of individuals who benefit from a counterfactual intervention, is identified.

部分识别中的约束资格条件AI 解读

The literature on stochastic programming typically restricts attention to problems that fulfill constraint qualifications. The literature on estimation and inference under partial identification frequently restricts the geometry of identified sets with diverse high-level assumptions. These superficially appear to be different approaches to closely related problems. We extensively analyze their relation. Among other things, we show that for partial identification through pure…

工具变量分位数回归模型的分解估计方法AI 解读

The instrumental variable quantile regression (IVQR) model (Chernozhukov and Hansen (2005)) is a popular tool for estimating causal quantile effects with endogenous covariates. However, estimation is complicated by the nonsmoothness and nonconvexity of the IVQR GMM objective function. This paper shows that the IVQR estimation problem can be decomposed into a set of conventional quantile regression subproblems which are convex and can be solved efficiently. This reformulation…

部分识别参数投影的置信区间AI 解读

We propose a bootstrap‐based calibrated projection procedure to build confidence intervals for single components and for smooth functions of a partially identified parameter vector in moment (in)equality models. The method controls asymptotic coverage uniformly over a large class of data generating processes. The extreme points of the calibrated projection confidence interval are obtained by extremizing the value of the function of interest subject to a proper relaxation of…

完全信息二元反应静态博弈中随机系数分布的非参数识别AI 解读

This paper studies binary response static games of complete information allowing complex heterogeneity through a random coefficients specification. The main result of the paper establishes nonparametric point identification of the joint density of all random coefficients except those on interaction effects. Under additional independence assumptions, we identify the joint density of the interaction coefficients. Moreover, we prove that in the presence of covariates that are…

区间删失变量下加权平均导数的渐近有效估计AI 解读

This paper studies the identification and estimation of weighted average derivatives of conditional location functionals including conditional mean and conditional quantiles in settings where either the outcome variable or a regressor is interval-valued. Building on Manski and Tamer (2002, Econometrica 70(2), 519–546) who study nonparametric bounds for mean regression with interval data, we characterize the identified set of weighted average derivatives of regression…

部分识别计量模型推断的对偶方法AI 解读

This paper considers inference for the set ΘI of parameter values that minimize a criterion function. Chernozhukov et al. (2007) (CHT) develop a general theory of estimation and inference using the level-set of a criterion function. We establish a dual relationship between the level-set estimator and its support function and show that the properly normalized support function provides alternative Wald-type inference methods. These methods can be used to obtain confidence sets…

不完整模型的稳健置信区域AI 解读

Call an economic model incomplete if it does not generate a probabilistic prediction even given knowledge of all parameter values.We propose a method of inference about unknown parameters for such models that is robust to heterogeneity and dependence of unknown form.The key is a Central Limit Theorem for belief functions; robust con…dence regions are then constructed in a fashion paralleling the classical approach.Monte Carlo simulations support tractability of the method…

部分识别模型的两阶段程序AI 解读

This paper studies a two-stage procedure for estimating partially identified models, based on Chernozhukov, Hong, and Tamer’s (2007) theory of set estimation and inference. We consider the case where a sub-vector of parameters or their identified set can be estimated separately from the rest, possibly subject to a priori restrictions. Our procedure constructs the second-stage set estimator and confidence set by taking appropriate level sets of a criterion function, using a…