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内生二元处理下适用于任意响应类型的工具残差估计AI 解读

Abstract Given an endogenous/confounded binary treatment D, a response Y with its potential versions (Y0, Y1) and covariates X, finding the treatment effect is difficult if Y is not continuous, even when a binary instrumental variable (IV) Z is available. We show that, for any form of Y (continuous, binary, mixed,…), there exists a decomposition Y = μ0(X) + μ1(X)D + error with E(error|Z,X) = 0, where μ1(X)≡E(Y1-Y0|complier,X) and ‘compliers’ are those who get treated if and…

利用倾向得分残差估计处理效应的简单最小二乘方法AI 解读

Propensity score matching is widely used to control covariates when analysing the effects of a nonrandomized binary treatment. However, it requires several arbitrary decisions, such as how many matched subjects to use and how to choose them. In this paper a simple least squares estimator is proposed, where the treatment, and possibly the response variable, is replaced by the propensity score residual. The proposed estimator controls covariates semiparametrically if the…

样本选择模型中的处理效应及其非参数估计AI 解读

In a sample-selection model with the ‘selection’ variable Q and the ‘outcome’ variable Y∗, Y∗ is observed only when Q=1. For a treatment D affecting both Q and Y∗, three effects are of interest: ‘participation’ (i.e., the selection) effect of D on Q, ‘visible performance’ (i.e., the observed outcome) effect of D on Y≡QY∗, and ‘invisible performance’ (i.e., the latent outcome) effect of D on Y∗. This paper shows the conditions under which the three effects are identified,…

随机删失结果的分布处理效应非参数检验AI 解读

Summary For a binary treatment ν = 0, 1 and the corresponding ‘potential response’Y0 for the control group (ν = 0) and Y1 for the treatment group (ν = 1), one definition of no treatment effect is that Y0 and Y1 follow the same distribution given a covariate vector X. Koul and Schick have provided a non-parametric test for no distributional effect when the realized response (1−ν)Y0+νY1 is fully observed and the distribution of X is the same across the two groups. This test is…

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随机试验中的中位数处理效应AI 解读

Summary For two response variables y t and y c corresponding to two treatments for two policies) T and C, we wish to learn about quantiles of y t−y c from the marginal quantiles of y t and y c; only one of y t and y c is observed for an individual. We find that, in general, this is difficult for quantiles other than the median unless strong assumptions are imposed on how y t is related to y c. For the median, we present conditions under which the sign of the median treatment…

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含受限内生解释变量的联立方程非参数两阶段估计AI 解读

Estimation of simultaneous equations with limited (or transformed) endogenous regressors has been difficult in the parametric literature for various reasons. In this paper, we propose a nonparametric two-stage method that is analogous to two-stage least-squares estimation. A simultaneous censored model is used to illustrate our approach, and then its generalization to other cases is developed. The technical highlight is in handling a nondifferentiable second-stage minimand…

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删失回归的缩尾均值估计量AI 解读

We introduce a semiparametric estimator for the censored linear regression model. It is based on the regression version of Huber's [6] M-estimator. It includes Powell's [19] censored least absolute deviations estimator as a special case and is related to Powell's [20] symmetrically censored least-squares estimator. We prove strong consistency and derive its asymptotic distribution which is √n-consistent with an easily computable covariance matrix. A small-scale simulation…

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