EXTENDING ECONOMIC MODELS WITH TESTABLE ASSUMPTIONS: THEORY AND APPLICATIONS
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This article studies the identification of complete economic models with testable assumptions. We start with a local average treatment effect ( L A T E $LATE$ upper L upper A upper T upper E ) model where the “No Defiers,” the independent IV assumption, and the exclusion restrictions can be jointly refuted by some data distributions. We propose two relaxed assumptions that are not refutable, with one assumption focusing on relaxing the “No Defiers” assumption while the other relaxes the independent IV assumption. The identified set of L A T E $LATE$ upper L upper A upper T upper E under either of the two relaxed assumptions coincides with the classical L A T E $LATE$ upper L upper A upper T upper E Wald ratio expression whenever the original assumption is not refuted by the observed data distribution. We propose an estimator for the identified L A T E $LATE$ upper L upper A upper T upper E and derive the estimator’s limit distribution. We then develop a general method to relax a refutable assumption A . This relaxation method requires finding a function that measures the deviation of an econometric structure from the original assumption A , and a relaxed assumption A ~ $\tilde {A}$ upper A overtilde is constructed using this measure of deviation. We characterize a condition to ensure the identified sets under A ~ $\tilde {A}$ upper A overtilde and A coincide whenever A is not refuted by the observed data distribution and discuss the criteria to choose among different relaxed assumptions.
正交化合成控制
When conducting inference for the average treatment effect on the treated with a Synthetic Control Estimator, the vector of control weights is a nuisance parameter that is often constrained, high-dimensional, and may be only partially identified even when the average treatment effect on the treated is point-identified. All three of these features of a nuisance parameter can lead to failure of asymptotic normality for the estimate of the parameter of interest when using…
条件线性规划聚合值的自适应估计
We develop a covariate-assisted approach to partially identified parameters that are solutions to an under-identified system of linear equations with known coefficients. Examples include bounds on treatment effects, models of unemployment with state dependence, choice-theoretic models of IV, and random utility models. The boundary (i.e., support function) of the proposed identified set is represented as an average of intersections of regression functions, aggregated over the…
未知系数线性系统的统计推断
This paper considers the problem of testing whether there exists a solution satisfying certain non-negativity constraints to a linear system of equations. Importantly and in contrast to some prior work, we allow all parameters in the system of equations, including the slope coefficients, to be unknown. For this reason, we describe the linear system as having unknown (as opposed to known) coefficients. This hypothesis testing problem arises naturally when constructing…
弱识别与少量聚类下工具变量分位数回归的梯度野生自助法
We study the gradient wild bootstrap-based inference for instrumental variable quantile regressions in the framework of a small number of large clusters in which the number of clusters is viewed as fixed, and the number of observations for each cluster diverges to infinity. For the Wald inference, we show that our wild bootstrap Wald test, with or without studentization using the cluster-robust covariance estimator (CRVE), controls size asymptotically up to a small error as…
通过最优传输部分识别工具变量下的政策相关处理效应
Policy-Relevant Treatment Effects (PRTEs) are generally not point-identified under standard Instrumental Variable (IV) assumptions when the instrument generates limited support in treatment propensity. We show that PRTE partial identification in the generalized Roy model can instead be formulated as a Constrained Conditional Optimal Transport (CCOT) problem over the joint conditional law of the potential outcome and the latent resistance. The resulting multidimensional CCOT…
非线性工具变量估计量的遗漏变量偏差量化
We develop a framework for quantifying omitted variable bias (OVB) in nonlinear instrumental variable (IV) estimators, including the local average treatment effect (LATE), the LATE for the treated (LATT), and the partially linear IV model (PLIVM). Extending sensitivity analysis beyond linear settings, we derive bias decompositions, establish partial identification bounds, and construct OVB-adjusted confidence intervals. We estimate OVB bounds and conduct inference using…