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农业政策干预的规模化效应AI 解读

Policies to raise agricultural productivity have been central in the fight against global poverty. Their impacts are often measured in experiments that provide strong causal identification but may be too small-scale to capture general equilibrium effects that arise when policies are scaled up. We develop a quantitative model of agricultural trade, featuring a granular economic geography, that combines parameters estimated from small-scale experiments with rich administrative…

警力资源的有效部署：来自印度随机酒驾严打的理论与新证据AI 解读

Should police activity be narrowly focused and high force, or widely dispersed but of moderate intensity? Critics of intense “hotspot” policing argue it primarily displaces, not reduces, crime. But if learning about enforcement takes time, the police may take advantage of that period to intervene intensively in the most productive location. We propose a multi-armed bandit model of criminal learning and structurally estimate its parameters using data from a randomized…

有序随机效用模型中参数假设的可检验含义AI 解读

We establish the testable implications of ordered random utility models under nonparametric, semi-nonparametric, and parametric assumptions. After characterizing these models in a continuous setting and in the absence of fundamental parametric restrictions, we show that assumptions about the type distribution alone are immaterial, while assumptions about the map linking types to utilities are relevant only insofar as they restrict the class of utilities at stake.…

分解加成率的上升AI 解读

A recent literature argues that the rise in aggregate markups is due to economic activity reallocating toward high-markup firms rather than an increase in firmlevel markups. I show that standard decomposition methods used to analyze this phenomenon can estimate a non-zero reallocation effect even when the joint distribution of markups and market shares is time-invariant. This is due to churning in the firm-level distribution of markups and market shares. Empirically, I find…

利用成交价格进行有限样本拍卖推断AI 解读

We first provide inference on the quantile function of the bid distribution in firstprice or descending auctions, and the quantile function of the value distribution in second-price or ascending auctions, with a known number of bidders with symmetric independent private values. Specifically, we provide finite-sample, nonparametric, tuning parameter-free, uniform confidence bands for these quantile functions when observing only the transaction price. We also show how these…

有限状态马尔可夫链近似：一种隐马尔可夫方法AI 解读

This paper proposes a novel finite-state Markov chain approximation method for Markov processes with continuous support, providing both an optimal grid and a transition probability matrix. The method can be used for multivariate processes and processes with time-varying components. It is based on minimizing the information loss between a Hidden Markov Model and the true data-generating process. We provide sufficient conditions under which this information loss can be made…

生育、子女性别与父母迁移决策：来自中国的证据AI 解读

We study how the number and gender of children affect rural-tourban parental migration in China. Exploiting variation from the “1.5- child policy,” which allowed a second birth only if the firstborn was a girl, we separately identify the causal effects of fertility and child gender based on a novel semi-parametric method. Using the 2010 population census, we find that an additional child increases the likelihood of joint parental migration by 12.1 percent, while the presence…

当回归变量被随机分配时的标准误AI 解读

We show that the OLS variance formula simplifies when values of the regressor of interest are assigned randomly and independently of other regressors. When the regressor of interest is independent not only of other regressors but also of the error term, the homoskedastic variance formula is valid even if regression errors are correlated. When the error term is heteroscedastic with respect to the regressor of interest, the variance formula has to be adjusted not only for…

条件影响函数AI 解读

There are many nonparametric objects of interest that are functionals of a conditional distribution, such as average treatment effects conditional on a low-dimensional set of covariates. We define a conditional influence function (CIF) as the Gateaux derivative of a conditional parameter with respect to perturbations of the conditional law of a single observation given the conditioning variable. CIFs generalize classical influence functions and can be used to (i) construct…

劳动者侧歧视：信念与偏好AI 解读

When agents choose among options with unobserved attributes to maximize expected utility, their choices conflate preferences with beliefs about those attributes. We develop a novel theory-driven identification strategy for such settings, showing that compensating differentials separately identify beliefs from preferences. Noting that workers’ preferences and beliefs shape labor market outcomes, yet remain understudied, we apply our method to provide the first evidence on the…

样本选择下观测数据中总体平均与分位处理效应的部分识别AI 解读

ABSTRACT This article partially identifies population treatment effects in observational data under both non‐random treatment assignment and sample selection. Bounds are provided for both average and quantile population treatment effects, combining assumptions for the selected and the non‐selected subsamples. We show how different assumptions help narrow identification regions, and we illustrate our methods by partially identifying the effect of maternal education on the…

ARMA-Design：部分可观测实验中 A/B 测试的最优处理分配AI 解读

Online experiments are frequently employed in technological companies to evaluate the performance of a newly developed policy, product, or treatment relative to a baseline control. In many applications, the experimental units receive a sequence of treatments over time. To handle these time-dependent settings, existing A/B testing solutions typically assume a fully observable experimental environment that satisfies the Markov condition. However, this assumption often does not…

用分布回归双重差分评估最低工资引入对工资、收入与工时分布的影响AI 解读

ABSTRACT We estimate the causal effects of the introduction of the minimum wage in Germany on the distributions of hourly wages, monthly earnings, and hours worked, combining rich large‐scale data sources. We compare linear and non‐linear versions of distribution regression difference‐in‐differences (DR‐DiD) and also estimate bivariate DR‐DiD. We show that the introduction of the minimum wage in 2015 led to shifts in low hourly wages around the minimum wage threshold…

前门模型下因果效应的灵活非参数推断AI 解读

Abstract Evaluating causal treatment effects in observational studies requires addressing confounding. While the back-door criterion enables identification through adjustment for observed covariates, it fails in the presence of unmeasured confounding. The front-door criterion offers an alternative by leveraging variables that fully mediate the treatment effect and are unaffected by unmeasured confounders of the treatment-outcome pair. We develop novel one-step and targeted…

结合协变量与稀疏内在得分的实体排序AI 解读

This paper addresses the item ranking problem with associated covariates, focusing on scenarios where the preference scores can not be fully explained by covariates, and the remaining intrinsic scores are sparse. Specifically, we extend the pioneering Bradley-Terry-Luce (BTL) model by incorporating covariate information and considering sparse individual intrinsic scores. Our work introduces novel model identification conditions and examines the statistical rates of the…

超越固定截断时点：临床试验中的自适应限制平均生存时间方法AI 解读

Summary Restricted mean survival time offers a compelling nonparametric alternative to hazard ratios for right-censored time-to-event data, particularly when the proportional hazards assumption is violated. By capturing the total event-free time over a specified horizon, it provides an intuitive and clinically meaningful measure of absolute treatment benefit. Nonetheless, selecting the restriction time poses challenges: choosing a small restriction time may overlook…

最优治疗规则下平均结局的最优推断AI 解读

Abstract When an optimal treatment regime (OTR) is considered, we need to evaluate the OTR in a valid and efficient way. The classical inference applied to the mean outcome under OTR, assuming the OTR is the same as the estimated OTR, might be biased when the regularity assumption that OTR is unique is violated. Although several methods have been proposed to allow nonregularity in such inference, its optimality is unclear due to challenges in deriving semiparametric…

矩阵分位数因子模型AI 解读

This paper introduces a matrix quantile factor model for matrix-valued data with low-rank structure. We estimate the row and column factor spaces via minimizing the empirical check loss function with orthogonal rotation constraints. We show that the estimates converge at rate (min{p1p2,p2T,p1T})−1/2 in the average Frobenius norm, where p1, p2 and T are the row dimensionality, column dimensionality and length of the matrix sequence, respectively. This rate is faster than that…

高维干扰参数下分位数回归高维效应的去相关检验AI 解读

Quantile regression provides a flexible framework in econometrics for modeling the relationship between a response variable and multivariate predictors, particularly when heterogeneous effects are present. This paper addresses the challenge of testing high-dimensional coefficients in quantile regression in the presence of high-dimensional nuisance parameters. We first extend a recently proposed score test by incorporating a convolution-type smoothed quantile loss, which…

利用大时间维度面板数据估计未观测的横截面网络连接AI 解读

We propose a simple three-step method to estimate the cross-sectional interdependence in a customary network econometric model with an unobserved network and a large T. First, we estimate the reduced-form model to construct instruments for the endogenous peer outcomes. Second, we use two-stage least squares to estimate the direct and contextual effects of the exogenous regressors, as well as the endogenous peer effect. Third, we estimate the network weight matrix. Monte…

面板MIDAS模型非线性最小二乘估计量的渐近分布AI 解读

The Mixed Data Sampling (MIDAS) approach provides a flexible framework for incorporating mixed-frequency data into econometric models. In this paper, we introduce the MIDAS framework into a panel data model and derive the asymptotic properties of the NLS estimator under a joint asymptotic framework where both N and T tend to infinity, allowing for heteroskedasticity and arbitrary correlation within each individual. To facilitate valid inference, we construct robust…

乘法工具变量模型AI 解读

Summary The instrumental variable (IV) design is a common approach to address hidden confounding bias. For validity, an IV must impact the outcome only through its association with the treatment. In addition, IV identification has required a homogeneity condition such as monotonicity or no unmeasured common effect modifier between the additive effect of the treatment on the outcome, and that of the IV on the treatment. In this work, we introduce the Multiplicative…

存在少报的有序选择模型中的变化中的变化AI 解读

We develop a Difference-in-Differences framework for discrete, ordered outcomes subject to underreporting. Such outcomes commonly arise in self-reported surveys on socially undesirable or stigmatized behaviors, where respondents may conceal their true behavior. For a discrete Changes-in-Changes model that is shown to admit an equivalent threshold-crossing representation, we derive nonparametric bounds for the counterfactual and factual outcome distributions as well as for…

主分层可忽略性下基于 U 统计量的主分层推断AI 解读

Abstract Principal stratification is a popular framework for causal inference in the presence of an intermediate outcome. While the principal average treatment effects are the standard target of inference, they may be insufficient when interest lies in the relative ordering of potential outcomes within a principal stratum. We introduce the principal generalized causal effect estimands to accommodate nonlinear contrast functions, providing robust, probability-scale summaries…