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半参数模型中的交叉拟合经验似然AI 解读

Summary We propose a new cross-fitted empirical likelihood (CFEL) inference procedure for low-dimensional parameters in the presence of complex, infinite dimensional nuisance parameters that may be estimated by modern machine-learning methods. Relying on the Neyman orthogonal moment condition and cross-fitting as a sample-splitting procedure, we show that our CFEL statistic is asymptotically pivotal under weak conditions often invoked in Wald-type approaches. Three examples…

说服研究中的二元工具变量模型：刻画依从者的说服类型AI 解读

Summary In an empirical study of persuasion, researchers often use a binary instrument to encourage individuals to consume information and take some action. We show that, with a binary Imbens–Angrist instrumental variable model and the monotone treatment response assumption, it is possible to identify the joint distribution of potential outcomes among compliers. This is necessary to identify the percentage of mobilized voters and their statistical characteristic defined by…

使Lee界更稳健且更紧：随机单调性与对称性下的样本选择模型AI 解读

Summary In the presence of sample selection, Lee’s (2009, Review of Economic Studies 76, 1071–102) non-parametric bounds are a popular tool for estimating a treatment effect. However, the Lee bounds rely on the monotonicity assumption, the empirical validity of which is sometimes unclear. Furthermore, the bounds are often regarded to be wide and less informative even under monotonicity. To address these issues, this study introduces a stochastic version of the monotonicity…

提高观察性区组设计敏感度的条件化策略AI 解读

Abstract In an observational block design, there are I blocks of J individuals, typically with one treated individual and J−1 controls; however, unlike a randomized block design, individuals were not randomly assigned to treatment or control. To be convincing, an observational block design must demonstrate that an ostensible treatment effect is not actually a consequence of small or moderate unmeasured biases of treatment assignment in the absence of a treatment effect. It…

面向处理效应异质性学习的自适应实验AI 解读

Abstract Understanding treatment effect heterogeneity has become an increasingly popular task in various fields, as it helps design personalized advertisements in e-commerce or targeted treatment in biomedical studies. However, most of the existing work in this research area focused on either analysing observational data based on strong causal assumptions or conducting post hoc analyses of randomized controlled trial data, and there has been limited effort dedicated to the…

半参数后验修正AI 解读

Abstract We present a new approach to semiparametric inference using corrected posterior distributions. The method allows us to leverage the adaptivity, regularization, and predictive power of nonparametric Bayesian procedures to estimate low-dimensional functionals of interest without being restricted by the holistic Bayesian formalism. Starting from a conventional posterior on the whole data-generating distribution, we correct the marginal posterior for each functional of…

交互项模型中的遗漏变量偏误：一则警示AI 解读

Abstract We highlight that analyses using interaction terms to study treatment effect heterogeneity are susceptible to a form of omitted variable bias that is often overlooked in economics. Unlike most instances of omitted variable bias, the omitted variables in this case are available to the researcher but were not included in the model. We demonstrate that this exclusion matters based on a replication of 205 estimates across seventeen papers published in the American…

选举中的执政更替AI 解读

Abstract In most national elections, voters face a key choice between continuity and change. Electoral turnovers occur when the incumbent candidate or party fails to win reelection. To understand how turnovers affect national outcomes, we study all presidential and parliamentary elections held globally between 1946 and 2018. We document the prevalence of turnovers over time and estimate their effects on economic performance, human development, and the quality of democracy.…

平均处理效应的回归调整插补估计AI 解读

Imputing missing potential outcomes using an estimated regression function is a natural idea for estimating causal effects. In the literature, estimators that combine imputation and regression adjustments are believed to be comparable to augmented inverse probability weighting. Accordingly, people for a long time conjectured that such estimators, while avoiding directly constructing the weights, are also doubly robust (Imbens, 2004; Stuart, 2010). Generalizing an earlier…

群组层面处理的分位数回归AI 解读

To study distributional effects of group level treatments, Chetverikov et al. (2016) proposed a grouped instrumental variables quantile regression estimator, a quantile extension of the Hausman and Taylor’s 1981 instrumental variables estimator for panel data. However, their approach only allows for heterogenous distributional effects of group-level treatments that correspond to individual-level unobserved characteristics, but not group-level unobserved characteristics. In…

回归不连续设计的统一诊断检验AI 解读

Diagnostic tests for regression discontinuity design face a size-control problem. We document a massive over-rejection of the diagnostic restriction among empirical studies in the top five economics journals. At least one diagnostic test was rejected for 19 out of 59 studies, whereas less than 5% of the collected 787 tests rejected the null hypotheses. In other words, one-third of the studies rejected at least one of their diagnostic tests, whereas their underlying…

交错处理设计中双向固定效应估计量的比较分析AI 解读

Two-way fixed effects (TWFE) is a flexible and widely used approach for estimating treatment effects, and several TWFE estimators have been proposed for staggered treatment designs. This paper focuses on the extended TWFE estimator, introduced by Borusyak et al. (2024) and Wooldridge (2021), and compares it with alternative TWFE estimators. The main contribution is the derivation of an equation that connects the extended TWFE estimator with the difference-in-differences…

广义 Lee 界限AI 解读

Lee (2009) is a common approach to bound the average causal effect in the presence of selection bias, assuming the treatment effect on selection has the same sign for all subjects. This paper generalizes Lee bounds to allow the sign of this effect to be identified by pretreatment covariates, relaxing the standard (unconditional) monotonicity to its conditional analog. Asymptotic theory for generalized Lee bounds is proposed in low-dimensional smooth and high-dimensional…

利用边际处理效应开展分层政策评估AI 解读

This paper proposes a unified approach to derive sharp bounds on conventional policy parameters when the instrumental variables (IVs) are potentially invalid. Using a vine copula approach, we propose a novel characterization of the identified sets for the marginal treatment effect (MTE) and the policy-relevant treatment effect (PRTE) parameters. Our method has various advantages: First, it explicitly demonstrates how imposing different IV-related assumptions with different…

二元结果随机实验精确置信区间的快速计算AI 解读

Given a randomized experiment with binary outcomes, exact confidence intervals for the average causal effect of the treatment can be computed through a series of permutation tests. This approach requires minimal assumptions and is valid for all sample sizes, as it does not rely on large-sample approximations such as those implied by the central limit theorem. We show that these confidence intervals can be found in O(nlogn) permutation tests in the case of balanced designs,…

基于马氏距离的最优选择再随机化渐近理论AI 解读

Rerandomization, a design that utilizes pretreatment covariates and improves their balance between different treatment groups, has received attention recently in both theory and practice. From a survey by Bruhn and McKenzie (2009), there are at least two types of rerandomization that are used in practice: the first rerandomizes the treatment assignment until covariate imbalance is below a prespecified threshold; the second randomizes the treatment assignment multiple times…

价格对分析师现金流预期的影响：主观信念数据与理性贴现率变动的协调AI 解读

I show that prices impact analyst cash flow expectations and argue this impact can partially reconcile subjective beliefs data with asset pricing models in which investors have rational expectations and discount rate variation drives prices. Previous work argues that correlations of biased analyst cash flow expectations with prices and future returns contradict rational models and imply biased investor expectations distort prices. However, using two instrumental variables…

非线性趋势结果的政策评价：美国新冠疫苗接种率AI 解读

ABSTRACT This paper discusses pitfalls in two way fixed effects (TWFE) regressions when the outcome variables contain nonlinear and possibly stochastic trend components. If a policy change shifts trend paths of outcome variables, TWFE estimation can distort results and invalidate inference, especially in a context of evolving policy decisions. A robust solution is proposed by allowing for dynamic club membership empirically using a relative convergence test procedure. The…

多维断点回归设计的操纵检验AI 解读

ABSTRACT The causal inference model for the regression discontinuity design (RDD) relies on assumptions that imply the continuity of the density of the assignment (running) variable. The test for this implication is commonly referred to as the manipulation test and is regularly reported in applied research to strengthen the design's validity. The multidimensional RDD (MRDD) extends the RDD to contexts where treatment assignment depends on several running variables. This…

面板数据政策评价方法的预测区间AI 解读

ABSTRACT We consider the inference on individual and time specific treatment effects on the treated within the framework of panel data approach for programme evaluation. We formulate the target problem as constructing prediction intervals for high‐dimensional linear regressions with weakly dependent data. Post‐LASSO OLS is used for estimation, while dependent wild bootstrap and simple residual bootstrap are used for the construction of prediction intervals. The proposed…

人力资本积累决定因素的部分识别：教师应用AI 解读

ABSTRACT This paper views career growth in teacher quality through the lens of human capital theory to understand the roles of on‐the‐job training (OJT) and learning by doing (LBD) in human capital formation. If OJT is the primary determinant of human capital, incentive pay policies could create a dynamic multitasking problem, leading teachers to reduce their human capital investments, thereby lowering future student achievement. In contrast, teacher human capital and future…

政策相关处理参数对单调性违背的敏感性AI 解读

ABSTRACT This paper proposes a method to bound policy‐relevant treatment parameters (PRTPs) when the monotonicity assumption that the IV affects individuals' treatment response in the same direction is weakened. The bounding framework uses the proportion of defiers relative to compliers as a sensitivity parameter and yields an identified set that is an interval. The method is illustrated in an empirical application where the same‐sex instrument was used to calculate the…

基于随机森林的面板数据政策评价方法AI 解读

ABSTRACT It is challenging to conduct controlled experiments to assess the impacts of social policy. To address this, past studies propose a panel data approach using factor models to estimate average treatment effects. The selection of control units is a critical step to balance the goodness of fit within‐sample with the posttreatment forecasting error when the number of observed potential control units is large. In this study, we propose using random forests, an ensemble…

“无需”统一重叠的策略学习：悲观原则与广义经验 Bernstein 不等式AI 解读

This paper studies offline policy learning, which aims at utilizing observations collected a priori (from either fixed or adaptively evolving behavior policies) to learn an optimal individualized decision rule that achieves the best overall outcomes for a given population. Existing policy learning methods rely on a uniform overlap assumption, that is, the propensities of exploring all actions for all individual characteristics must be lower bounded; the performance of the…