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改进的洛伦兹占优非参数自助检验AI 解读

One income or wealth distribution is said to Lorenz dominate another when the Lorenz curve for the former is nowhere below that of the latter, indicating a (weakly) more equitable allocation of resources. Existing tests of the null of Lorenz dominance based on pairs of samples of income or wealth achieve the nominal rejection rate asymptotically when the two Lorenz curves are equal, but are conservative at other null configurations. We propose new nonparametric bootstrap…

分位数因子模型AI 解读

Quantile factor models (QFM) represent a new class of factor models for high‐dimensional panel data. Unlike approximate factor models (AFM), which only extract mean factors, QFM also allow unobserved factors to shift other relevant parts of the distributions of observables. We propose a quantile regression approach, labeled Quantile Factor Analysis (QFA), to consistently estimate all the quantile‐dependent factors and loadings. Their asymptotic distributions are established…

分位数回归模型因变量中的测量误差AI 解读

We study the consequences of measurement error in the dependent variable of random‐coefficients models, focusing on the particular case of quantile regression. The popular quantile regression estimator of Koenker and Bassett (1978) is biased if there is an additive error term. Approaching this problem as an errors‐in‐variables problem where the dependent variable suffers from classical measurement error, we present a sieve maximum likelihood approach that is robust to…

合理怀疑：岗位层面就业歧视的实验检测AI 解读

This paper develops methods for detecting discrimination by individual employers using correspondence experiments that send fictitious resumes to real job openings. We establish identification of higher moments of the distribution of job‐level callback rates as a function of the number of resumes sent to each job and propose shape‐constrained estimators of these moments. Applying our methods to three experimental data sets, we find striking job‐level heterogeneity in the…

理性预期的相容性：刻画与检验AI 解读

In this paper, we build a new test of rational expectations based on the marginal distributions of realizations and subjective beliefs. This test is widely applicable, including in the common situation where realizations and beliefs are observed in two different data sets that cannot be matched. We show that whether one can rationalize rational expectations is equivalent to the distribution of realizations being a mean‐preserving spread of the distribution of beliefs. The…

一般处理模型有效估计的统一框架AI 解读

This paper presents a weighted optimization framework that unifies the binary, multivalued, and continuous treatment—as well as mixture of discrete and continuous treatment—under a unconfounded treatment assignment. With a general loss function, the framework includes the average, quantile, and asymmetric least squares causal effect of treatment as special cases. For this general framework, we first derive the semiparametric efficiency bound for the causal effect of…

工具变量分位数回归模型的分解估计方法AI 解读

The instrumental variable quantile regression (IVQR) model (Chernozhukov and Hansen (2005)) is a popular tool for estimating causal quantile effects with endogenous covariates. However, estimation is complicated by the nonsmoothness and nonconvexity of the IVQR GMM objective function. This paper shows that the IVQR estimation problem can be decomposed into a set of conventional quantile regression subproblems which are convex and can be solved efficiently. This reformulation…

处理效应分布的部分识别及其在 KIPP 项目中的应用AI 解读

We bound the distribution of treatment effects under plausible and testable assumptions on the joint distribution of potential outcomes, namely that potential outcomes are mutually stochastically increasing. We show how to test the empirical restrictions implied by those assumptions. The resulting bounds substantially sharpen bounds based on classical inequalities. We apply our method to estimate bounds on the distribution of effects of attending a Knowledge is Power Program…

边际独立与条件独立的检验AI 解读

Summary We consider testing marginal independence versus conditional independence in a trivariate Gaussian setting. The two models are nonnested, and their intersection is a union of two marginal independences. We consider two sequences of such models, one from each type of independence, that are closest to each other in the Kullback–Leibler sense as they approach the intersection. They become indistinguishable if the signal strength, as measured by the product of two…

预测回归中条件风险价值的统计推断AI 解读

Conditional value-at-risk is a popular risk measure in risk management. We study the inference problem of conditional value-at-risk under a linear predictive regression model. We derive the asymptotic distribution of the least squares estimator for the conditional value-at-risk. Our results relax the model assumptions made in (Oper. Res. 60 (2012) 739–756) and correct their mistake in the asymptotic variance expression. We show that the asymptotic variance depends on the…

存在协变量时的分位数处理效应AI 解读

This paper proposes a method to estimate unconditional quantile treatment effects (QTEs) given one or more treatment variables, which may be discrete or continuous, even when it is necessary to condition on covariates. The estimator, generalized quantile regression (GQR), is developed in an instrumental variable framework for generality to permit estimation of unconditional QTEs for endogenous policy variables, but it is also applicable in the conditionally exogenous case.…

回归折点设计中的分位数处理效应AI 解读

The literature on regression kink designs develops identification results for average effects of continuous treatments (Nielsen et al., 2010, American Economic Journal: Economic Policy 2, 185–215; Card et al., 2015, Econometrica 83, 2453–2483), average effects of binary treatments (Dong, 2018, Jump or Kink? Identifying Education Effects by Regression Discontinuity Design without the Discontinuity), and quantile-wise effects of continuous treatments (Chiang and Sasaki, 2019,…

相关随机系数面板模型中处理状态不变者的平均处理效应AI 解读

Summary Correlated random coefficient (CRC) models provide a useful framework for estimating average treatment effects (ATE) with panel data by accommodating heterogeneous treatment effects and flexible patterns of selection. In their simplest form, they lead to the well‐known difference‐in‐differences estimator. CRC models yield estimates of ATE for “movers” (i.e., cross‐sectional units whose treatment status changed over time) while ATE for “stayers” (i.e., cross‐sectional…

不对可观测数据施加额外限制的灵活观察性研究敏感性分析AI 解读

A fundamental challenge in observational causal inference is that assumptions about unconfoundedness are not testable from data. Assessing sensitivity to such assumptions is therefore important in practice. Unfortunately, some existing sensitivity analysis approaches inadvertently impose restrictions that are at odds with modern causal inference methods, which emphasize flexible models for observed data. To address this issue, we propose a framework that allows (1) flexible…

删失、选择与不依从下持续时间结果的平均与分位数处理效应界AI 解读

We consider the problem of assessing the effects of a treatment on duration outcomes using data from a randomized evaluation with noncompliance. For such settings, we derive nonparametric sharp bounds for average and quantile treatment effects addressing three pervasive problems simultaneously: self-selection into the spell of interest, endogenous censoring of the duration outcome, and noncompliance with the assigned treatment. Ignoring any of these issues could yield biased…

平滑非参数多元混合数据的位置—尺度检验AI 解读

A number of tests have been proposed for assessing the location-scale assumption that is often invoked by practitioners. Existing approaches include Kolmogorov–Smirnov and Cramer–von Mises statistics that each involve measures of divergence between unknown joint distribution functions and products of marginal distributions. In practice, the unknown distribution functions embedded in these statistics are typically approximated using nonsmooth empirical distribution functions…

经济流动性的部分识别：美国应用AI 解读

The economic mobility of individuals and households is of fundamental interest. While many measures of economic mobility exist, reliance on transition matrices remains pervasive due to simplicity and ease of interpretation. However, estimation of transition matrices is complicated by the well-acknowledged problem of measurement error in self-reported and even administrative data. Existing methods of addressing measurement error are complex, rely on numerous strong…

存在干扰参数时的局部回归推断AI 解读

We consider inference based on local estimating equations in the presence of nuisance parameters. The framework is useful for a number of applications including those in economic policy evaluation based on discontinuities or kinks and in real-time financial risk management. We focus on the criterion-function-based (in particular, empirical likelihood-based) inference, and establish conditions under which the test statistic has a pivotal asymptotic distribution. In the key…

比较可能误设的预测AI 解读

Recent work has emphasized the importance of evaluating estimates of a statistical functional (such as a conditional mean, quantile, or distribution) using a loss function that is consistent for the functional of interest, of which there is an infinite number. If forecasters all use correctly specified models free from estimation error, and if the information sets of competing forecasters are nested, then the ranking induced by a single consistent loss function is sufficient…

约束似然比检验的自适应临界值AI 解读

Summary We present a new general method for constrained likelihood ratio testing which, when few constraints are violated, improves upon the existing approach in the literature that compares the likelihood ratio with the quantile of a mixture of chi-squared distributions; the improvement is in terms of both simplicity and power. The proposed method compares the constrained likelihood ratio statistic against the quantile of only one chi-squared random variable with…

时间序列水平变化检测的稳健方法AI 解读

Summary We present a robust and nonparametric test for the presence of a changepoint in a time series, based on the two-sample Hodges–Lehmann estimator. We develop new limit theory for a class of statistics based on two-sample U-quantile processes in the case of short-range dependent observations. Using this theory, we derive the asymptotic distribution of our test statistic under the null hypothesis of a constant level. The proposed test shows better overall performance…

异质性处理效应模型中的双向排除限制AI 解读

Summary In this paper, we propose a novel method to identify the conditional average treatment effect partial derivative (CATE-PD) in an environment in which the treatment is endogenous, the treatment effect is heterogeneous, the candidate 'instrumental variables' can be correlated with latent errors, and the treatment selection does not need to be (weakly) monotone. We show that CATE-PD is point-identified under mild conditions if two-way exclusion restrictions exist: (a)…

具有多个阈值的回归不连续设计AI 解读

Numerous empirical studies employ regression discontinuity designs with multiple cutoffs and heterogeneous treatments. A common practice is to normalize all the cutoffs to zero and estimate one effect. This procedure identifies the average treatment effect (ATE) on the observed distribution of individuals local to existing cutoffs. However, researchers often want to make inferences on more meaningful ATEs, computed over general counterfactual distributions of individuals,…

固定效应分位数回归的无偏渐近正态性AI 解读

Nonlinear panel data models with fixed individual effects provide an important set of tools for describing microeconometric data. In a large class of such models (including probit, proportional hazard and quantile regression to name just a few) it is impossible to difference out the individual effects, and inference is usually justified in a ‘large n large T’ asymptotic framework. However, there is a considerable gap in the type of assumptions that are currently imposed in…