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经济学中的实证策略：照亮从原因到结果的路径AI 解读

The view that empirical strategies in economics should be transparent and credible now goes almost without saying. By revealing for whom particular instrumental variables (IV) estimates are valid, the local average treatment effects (LATE) framework helped make this so. This lecture uses empirical examples, mostly involving effects of charter and exam school attendance, to illustrate the value of the LATE framework for causal inference. LATE distinguishes independence…

打破同序：回归不连续设计与市场设计的结合AI 解读

Many schools in large urban districts have more applicants than seats. Centralized school assignment algorithms ration seats at over‐subscribed schools using randomly assigned lottery numbers, non‐lottery tie‐breakers like test scores, or both. The New York City public high school match illustrates the latter, using test scores and other criteria to rank applicants at the city's screened schools, combined with lottery tie‐breaking at the rest. We show how to identify causal…

货币政策效应的半参数估计：重新审视“推绳子”理论AI 解读

We develop flexible semiparametric time series methods for the estimation of the causal effect of monetary policy on macroeconomic aggregates. Our estimator captures the average causal response to discrete policy interventions in a macrodynamic setting, without the need for assumptions about the process generating macroeconomic outcomes. The proposed estimation strategy, based on propensity score weighting, easily accommodates asymmetric and nonlinear responses. Using this…

远离断点的考试选拔学校效应：回归不连续识别与估计AI 解读

In regression discontinuity (RD) studies exploiting an award or admissions cutoff, causal effects are nonparametrically identified for those near the cutoff. The effect of treatment on inframarginal applicants is also of interest, but identification of such effects requires stronger assumptions than those required for identification at the cutoff. This article discusses RD identification and estimation away from the cutoff. Our identification strategy exploits the…

We develop semiparametric tests for conditional independence in time series models of causal effects. Our approach is motivated by empirical studies of monetary policy effects and is semiparametric in the sense that we model the process determining the distribution of treatment—the policy propensity score—but leave the model for outcomes unspecified. A conceptual innovation is that we adapt the cross-sectional potential outcomes framework to a time series setting. We also…

In markets where prices are determined by the intersection of supply and demand curves, standard identification results require the presence of instruments that shift one curve but not the other. These results are typically presented in the context of linear models with fixed coefficients and additive residuals. The first contribution of this paper is an investigation of the consequences of relaxing both the linearity and the additivity assumption for the interpretation of…

利用参军申请者的社会保障数据估计志愿兵役的劳动力市场影响AI 解读

Effects of military service on veterans' earnings are estimated using Social Security administrative records to fit models with heterogeneous potential outcomes. The first estimation strategy uses matching and regression to compare applicants who enlisted with applicants who did not enlist. The second uses instrumental variables generated by an error in military entrance exams. The empirical results suggest that military service led to higher employment rates for veterans.…

使用工具变量识别因果效应AI 解读

We outline a framework for causal inference in settings where assignment to a binary treatment is ignorable, but compliance with the assignment is not perfect so that the receipt of treatment is nonignorable. To address the problems associated with comparing subjects by the ignorable assignment—an “intention-to-treat analysis”—we make use of instrumental variables, which have long been used by economists in the context of regression models with constant treatment effects. We…

可变处理强度模型中平均因果效应的两阶段最小二乘估计AI 解读

Two-stage least squares (TSLS) is widely used in econometrics to estimate parameters in systems of linear simultaneous equations and to solve problems of omitted-variables bias in single-equation estimation. We show here that TSLS can also be used to estimate the average causal effect of variable treatments such as drug dosage, hours of exam preparation, cigarette smoking, and years of schooling. The average causal effect in which we are interested is a conditional…

教育回报的分样本工具变量估计AI 解读

Two-stage least squares is biased in the same direction as ordinary least squares even in very large samples. The authors propose a split-sample instrumental variables estimator that is not biased toward ordinary least squares. Split-sample instrumental variables uses one-half of a sample to estimate parameters of the first-stage equation. Estimated first-stage parameters are then used to construct fitted values and second-stage parameter estimates in the other half sample.…

局部平均处理效应的识别与估计AI 解读

We investigate conditions sufficient for identification of average treatment effects using instrumental variables. First we show that the existence of valid instruments is not sufficient to identify any meaningful average treatment effect. We then establish that the combination of an instrument and a condition on the relation between the instrument and the participation status is sufficient for identification of a local average treatment effect for those who can be induced…

入学年龄对教育程度的影响：结合两样本矩的工具变量应用AI 解读

We present a model in which compulsory school attendance laws, which typically require school attendance until a specified birthday, induce a relationship between years of schooling and age at school entry. Variation in school starting age created by children's dates of birth provides a natural experiment for estimating the effect of age at school entry. Because no large data set contains information on both age at school entry and educational attainment, we use an…

The goodness-of-fit statistic for fixed effects models is shown to simplify to the degrees of freedom times the R(superscript "2") from a regression of analysis of covariance residuals on right hand side variables. This result is applied to test models for union wage effects. Identification and estimation of the return to schooling in models with fixed effects is also discussed. Analysis of covariance estimates of the returns to schooling are precisely estimated and roughly…

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