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存在样本选择与自选择的二元响应面板数据模型AI 解读

Summary We consider estimating binary response models on an unbalanced panel, where the outcome of the dependent variable may be missing due to nonrandom selection, or there is self‐selection into a treatment. In the present paper, we first consider estimation of sample selection models and treatment effects using a fully parametric approach, where the error distribution is assumed to be normal in both primary and selection equations. Arbitrary time dependence in errors is…

以因果模型选择识别犯罪的动态溢出AI 解读

Does crime in a neighborhood cause future crime? Without a source of quasi‐experimental variation in local crime, we develop an identification strategy that leverages a recently developed test of exogeneity (Caetano (2015)) to select a feasible regression model for causal inference. Using a detailed incident‐based data set of all reported crimes in Dallas from 2000 to 2007, we find some evidence of dynamic spillovers within certain types of crimes, but no evidence that…

尖锐断点回归估计量的两侧最优带宽联合选择AI 解读

A new bandwidth selection method that uses different bandwidths for the local linear regression estimators on the left and the right of the cut‐off point is proposed for the sharp regression discontinuity design estimator of the average treatment effect at the cut‐off point. The asymptotic mean squared error of the estimator using the proposed bandwidth selection method is shown to be smaller than other bandwidth selection methods proposed in the literature. The approach…

We revisit the classic semi‐parametric problem of inference on a low‐dimensional parameter θ0 in the presence of high‐dimensional nuisance parameters η0. We depart from the classical setting by allowing for η0 to be so high‐dimensional that the traditional assumptions (e.g. Donsker properties) that limit complexity of the parameter space for this object break down. To estimate η0, we consider the use of statistical or machine learning (ML) methods, which are particularly…

单侧检验中控制错误发现率的预知与自适应方法：理论及处理效应评价应用AI 解读

Economists are often interested in identifying effective policies or treatments together with subpopulations of individuals who respond positively (or with a sign that is expected) to these treatment interventions. In this paper, we propose an optimal false discovery rate controlling method that is especially useful for such one‐sided testing problems. The proposed procedure is optimal in the sense of minimizing the false non‐discovery rate while controlling the false…

内生样本选择下的非参数估计AI 解读

This paper addresses the problem of estimation of a nonparametric regression function from selectively observed data when selection is endogenous. Our approach relies on independence between covariates and selection conditionally on potential outcomes. Endogeneity of regressors is also allowed for. In the exogenous and endogenous case, consistent two-step estimation procedures are proposed and their rates of convergence are derived. Pointwise asymptotic distribution of the…

对称性限制下样本选择模型的非参数识别与估计AI 解读

Under a conditional mean restriction Das et al. (2003) considered nonparametric estimation of sample selection models. However, their method can only identify the outcome regression function up to a constant. In this paper we strengthen the conditional mean restriction to a symmetry restriction under which selection biases due to selection on unobservables can be eliminated through proper matching of propensity scores; consequently we are able to identify and obtain…

税收与高管薪酬：来自股票期权的证据AI 解读

Understanding the effects of taxes on executive compensation provides insight into the process determining this compensation and is a key input to top income tax rate policy. A 2010 tax reform in Canada, which greatly increased the effective tax rate on stock option compensation for a subset of firms, provides a natural experiment with which to address this issue. Difference-in-differences estimates suggest that this tax increase resulted in an immediate reduction in both…

平均处理效应估计的一般双重稳健性结果AI 解读

In this paper we study doubly robust estimators of various average and quantile treatment effects under unconfoundedness; we also consider an application to a setting with an instrumental variable. We unify and extend much of the recent literature by providing a very general identification result which covers binary and multi-valued treatments; unnormalized and normalized weighting; and both inverse-probability weighted (IPW) and doubly robust estimators. We also allow for…

存在干扰时两阶段随机实验的分析AI 解读

Two-stage randomization is a powerful design for estimating treatment effects in the presence of interference; that is, when one individual’s treatment assignment affects another individual’s outcomes. Our motivating example is a two-stage randomized trial evaluating an intervention to reduce student absenteeism in the School District of Philadelphia. In that experiment, households with multiple students were first assigned to treatment or control; then, in treated…

通过倾向得分加权平衡协变量AI 解读

Covariate balance is crucial for unconfounded descriptive or causal comparisons. However, lack of balance is common in observational studies. This article considers weighting strategies for balancing covariates. We define a general class of weights—the balancing weights—that balance the weighted distributions of the covariates between treatment groups. These weights incorporate the propensity score to weight each group to an analyst-selected target population. This class…

含内生解释变量的半参数空间自回归模型：犯罪数据应用AI 解读

This study considers semiparametric spatial autoregressive models that allow for endogenous regressors, as well as the heterogenous effects of these regressors across spatial units. For the model estimation, we propose a semiparametric series generalized method of moments estimator. We establish that the proposed estimator is both consistent and asymptotically normal. As an empirical illustration, we apply the proposed model and method to Tokyo crime data to estimate how the…

基于随机生成辅助变量的统计推断AI 解读

Summary In most real life studies, auxiliary variables are available and are employed to explain and understand missing data patterns and to evaluate and control causal relationships with variables of interest. Usually their availability is assumed to be a fact, even if the variables are measured without the objectives of the study in mind. As a result, inference with missing data and causal inference require some assumptions that cannot easily be validated or checked. In…

受控实验设计中的最优事前平衡AI 解读

Summary We develop a unified theory of designs for controlled experiments that balance baseline covariates a priori (before treatment and before randomization) using the framework of minimax variance and a new method called kernel allocation. We show that any notion of a priori balance must go hand in hand with a notion of structure, since with no structure on the dependence of outcomes on baseline covariates complete randomization (no special covariate balance) is always…

基于核的联合独立性检验AI 解读

Summary We investigate the problem of testing whether d possibly multivariate random variables, which may or may not be continuous, are jointly (or mutually) independent. Our method builds on ideas of the two-variable Hilbert–Schmidt independence criterion but allows for an arbitrary number of variables. We embed the joint distribution and the product of the marginals in a reproducing kernel Hilbert space and define the d-variable Hilbert–Schmidt independence criterion dHSIC…

处理状态仅在一个时期可观测时的双重差分AI 解读

Summary This paper considers the difference‐in‐differences (DID) method when the data come from repeated cross‐sections and the treatment status is observed either before or after the implementation of a program. We propose a new method that point‐identifies the average treatment effect on the treated (ATT) via a DID method when there is at least one proxy variable for the latent treatment. Key assumptions are the stationarity of the propensity score conditional on the proxy…

多值处理效应的识别AI 解读

Multivalued treatment models have typically been studied under restrictive assumptions: ordered choice, and more recently, unordered monotonicity. We show how treatment effects can be identified in a more general class of models that allows for multidimensional unobserved heterogeneity. Our results rely on two main assumptions: treatment assignment must be a measurable function of threshold‐crossing rules, and enough continuous instruments must be available. We illustrate…

利用工具变量推断政策相关处理参数AI 解读

We propose a method for using instrumental variables (IV) to draw inference about causal effects for individuals other than those affected by the instrument at hand. Policy relevance and external validity turn on the ability to do this reliably. Our method exploits the insight that both the IV estimand and many treatment parameters can be expressed as weighted averages of the same underlying marginal treatment effects. Since the weights are identified, knowledge of the IV…

一类回归模型中的最优推断AI 解读

We consider the problem of constructing confidence intervals (CIs) for a linear functional of a regression function, such as its value at a point, the regression discontinuity parameter, or a regression coefficient in a linear or partly linear regression. Our main assumption is that the regression function is known to lie in a convex function class, which covers most smoothness and/or shape assumptions used in econometrics. We derive finite‐sample optimal CIs and sharp…

谁应接受处理？处理选择的经验福利最大化方法AI 解读

One of the main objectives of empirical analysis of experiments and quasi‐experiments is to inform policy decisions that determine the allocation of treatments to individuals with different observable covariates. We study the properties and implementation of the Empirical Welfare Maximization (EWM) method, which estimates a treatment assignment policy by maximizing the sample analog of average social welfare over a class of candidate treatment policies. The EWM approach is…

条件部分独立下的处理效应识别AI 解读

Conditional independence of treatment assignment from potential outcomes is a commonly used but nonrefutable assumption. We derive identified sets for various treatment effect parameters under nonparametric deviations from this conditional independence assumption. These deviations are defined via a conditional treatment assignment probability, which makes it straightforward to interpret. Our results can be used to assess the robustness of empirical conclusions obtained under…

无序单调性AI 解读

This paper defines and analyzes a new monotonicity condition for the identification of counterfactuals and treatment effects in unordered discrete choice models with multiple treatments, heterogeneous agents, and discrete‐valued instruments. Unordered monotonicity implies and is implied by additive separability of choice of treatment equations in terms of observed and unobserved variables. These results follow from properties of binary matrices developed in this paper. We…

处理变量误测且内生时的异质性处理效应AI 解读

This paper studies the identifying power of an instrumental variable in the nonparametric heterogeneous treatment effect framework when a binary treatment is mismeasured and endogenous. Using a binary instrumental variable, I characterize the sharp identified set for the local average treatment effect under the exclusion restriction of an instrument and the deterministic monotonicity of the true treatment in the instrument. Even allowing for general measurement error (e.g.,…

断点回归何时有效？来自随机选举结果的证据AI 解读

We use elections data in which a large number of ties in vote counts between candidates are resolved via a lottery to study the personal incumbency advantage. We benchmark non‐experimental regression discontinuity design (RDD) estimates against the estimate produced by this experiment that takes place exactly at the cutoff. The experimental estimate suggests that there is no personal incumbency advantage. In contrast, conventional local polynomial RDD estimates suggest a…