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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…

含结构变化的空间面板数据模型AI 解读

Spatial panel data models have gained widespread application in social sciences, particularly in economics, due to their ability to capture spatial dependencies. However, existing studies on spatial models typically rely on the assumption of parameter stability, which may be overly restrictive given the well-documented prevalence of structural changes in economic relationships. This paper addresses this limitation by proposing and analyzing spatial panel data models that…

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…

未知异方差或工具变量下的高维二元选择模型AI 解读

This paper proposes a new method for estimating high-dimensional binary choice models. We consider a semiparametric model that places no distributional assumptions on the error term, allows for heteroskedastic errors, and permits endogenous regressors. Our approaches extend the special regressor estimator originally proposed by Lewbel (2000). This estimator becomes impractical in high-dimensional settings due to the curse of dimensionality associated with high-dimensional…

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…

具有近最优收敛率的动态空间自回归稳健估计AI 解读

Spatial autoregression has been extensively studied in various applications, yet its robust estimation methods have received limited attention. In this work, we introduce two dynamic spatial autoregression (DSAR) models aimed at capturing temporal trends and depicting the asymmetric network effects of the units. For both DSAR models, we propose a truncated Yule–Walker estimation method, which is tailored to achieve robust estimation in the presence of heavy-tailed data.…

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,…

分位数回归估计量的偏差修正AI 解读

We study the bias of classical quantile regression and instrumental variable quantile regression estimators. While being asymptotically first-order unbiased, these estimators can have non-negligible second-order biases. We derive a higher-order stochastic expansion of these estimators using empirical process theory. Based on this expansion, we derive an explicit formula for the second-order bias and propose a feasible bias correction procedure that uses finite-difference…

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…

删失选择下的分布回归AI 解读

Chernozhukov, Fernández-Val, and Luo (2023, CFL (2023) hereafter) considered a distribution regression model subject to sample selection with a binary selection mechanism. In this paper, we show how to identify and estimate a semi-parametric distribution regression model subject to a censored selection rule. With censored selection, we do not need to impose the usual outcome exclusion restriction or exclusion of the level of the latent selection variable from the selection…

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…

相关误差对增长脆弱性结论的挑战AI 解读

ABSTRACT We examine the impact of correlated errors on findings regarding fluctuations in US GDP growth, which are primarily driven by the lower quantiles of the distribution as predicted by financial conditions. A proposed zero‐correlation test reveals the presence of correlated residuals in the quantile regression, introducing endogeneity and raising concerns about the validity of the previous analysis. In response, we extend the model by including two additional lags of…

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…

内生网络中二元结果的同伴效应：战略互补与从众偏好AI 解读

ABSTRACT This paper introduces a generalized model of peer effects for binary outcomes, based on a network game that accounts for strategic complementarity (influence of the number of peers that select the same action) and conformity to social norms (penalizing deviations from the average peers' action). We derive conditions for a unique Bayesian Nash equilibrium under rational expectations about peers' behavior. Unobserved individual heterogeneity affecting network…

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…

因子模型的张量主成分分析AI 解读

Modern empirical analysis often relies on high-dimensional panel datasets with non-negligible cross-sectional and time-series correlations. Factor models are natural for capturing such dependencies. A tensor factor model describes the $d$-dimensional panel as a sum of a reduced rank component and an idiosyncratic noise, generalizing traditional factor models for two-dimensional panels. We consider a tensor factor model corresponding to the notion of a reduced multilinear…

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“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…