每日简报

内生性检验：一种基于矩的贝叶斯方法AI 解读

A standard assumption in the Bayesian estimation of linear regression models is that the regressors are exogenous in the sense that they are uncorrelated with the model error term. In practice, however, this assumption can be invalid. In this article, using the exponentially tilted empirical likelihood (ETEL) framework, we develop a Bayes factor test for endogeneity that compares a base model that is correctly specified under exogeneity but misspecified under endogeneity…

空间处理的因果推断AI 解读

Many events and policies (treatments) occur at specific spatial locations, with researchers interested in their effects on nearby units. I approach the spatial treatment setting from an experimental perspective: What ideal experiment would we design to estimate the causal effects of spatial treatments? This perspective motivates a comparison between units near realized treatment locations and units near counterfactual (unrealized) candidate locations, which differs from…

个性化政策的机制设计：激励运动的实地实验AI 解读

Personalizing policies can theoretically increase their effectiveness. However, personalization is difficult when individual types are unobservable and the preferences of policymakers and individuals are not aligned, which could cause individuals to misreport their type. Mechanism design offers a strategy to overcome this issue: offer an “incentive‐compatible” menu of policy choices designed to induce participants to select the variant intended for their type. Using a field…

农村移民与城市非正规就业：来自巴西的证据AI 解读

This paper studies the economic effects of rural‐urban migration on Brazilian cities. Using a shift‐share IV design, we show that, over a decade, drought‐induced immigration reduces informality, has no effect on unemployment, and increases the number of formal firms and jobs. Downward formal wage adjustments play a key role, as these long‐run effects are weaker in regions with stronger wage rigidity. In the short run, when wage rigidity is strongest, we replicate the…

线性面板模型中固定效应的最优收缩估计AI 解读

Shrinkage methods are frequently used to improve the precision of least squares estimators of fixed effects. However, widely used shrinkage estimators guarantee improved precision only under strong distributional assumptions. I develop an estimator for the fixed effects that obtains the best possible mean squared error within a class of shrinkage estimators. This class includes conventional shrinkage estimators and the optimality does not require distributional assumptions.…

延迟接受算法的实验评估：来自逾百个军官劳动力市场的证据AI 解读

Internal labor markets are increasingly important for matching workers to jobs within organizations. We present evidence from a randomized trial that compares matching workers to jobs using the deferred acceptance (DA) algorithm to the traditional manager‐directed matching process. Our setting is the U.S. Army's internal labor market, which matches over 14,000 officers to units annually. We find that DA reduces administrative burden and increases match quality as measured by…

细粒度情境中的空间经济学AI 解读

We examine the application of quantitative spatial models to the growing body of fine spatial data used to study local economic outcomes. In granular settings in which people choose from a large set of potential residence‐workplace pairs, observed outcomes in part reflect idiosyncratic choices. Using analytical examples, Monte Carlo simulations, and event studies of neighborhood employment booms, we demonstrate that calibration procedures that equate observed shares and…

基尼系数的子群分解：老问题的新解法AI 解读

We derive a novel decomposition of the Gini coefficient into within‐ and between‐group inequality terms that sum to the aggregate Gini coefficient. This decomposition is derived from a set of axioms that ensure desirable behavior for the within‐ and between‐group inequality terms. The decomposition of the Gini coefficient is unique given our axioms, easy to compute, and can be interpreted geometrically.

网络干扰下因果推断的局部方法AI 解读

We propose a new nonparametric modeling framework for causal inference when outcomes depend on how agents are linked in a social or economic network. Such network interference describes a large literature on treatment spillovers, social interactions, social learning, information diffusion, disease and financial contagion, social capital formation, and more. Our approach works by first characterizing how an agent is linked in the network using the configuration of other…

连续时间动态离散选择博弈的识别与估计AI 解读

This paper considers the theoretical, computational, and econometric properties of continuous‐time dynamic discrete choice games with stochastically sequential moves, introduced by Arcidiacono, Bayer, Blevins, and Ellickson (2016). We consider identification of the rate of move arrivals, which was assumed to be known in previous work, as well as a generalized version with heterogeneous move arrival rates. We reestablish conditions for existence of a Markov perfect…

满意化行为、加总与拟线性效用AI 解读

This paper studies approximation error in the quasilinear utility model. Error arises because individuals do not perfectly optimize, and instead satisfice. We investigate the consequences of individual satisficing for modeling aggregate demand, providing an approximate aggregation theorem. We present a simple method for statistical inference on the minimal level of satisficing needed to explain aggregate data. In an illustrative application to scanner panel data, we find…

潜在状态测量不完善时的条件选择概率估计AI 解读

Traditional dynamic discrete choice model—Conditional Choice Probability estimator (DDCM‐CCP) literature considered identification using the likelihood of longitudinal choice only. However, such an approach has a limitation in identifying flexible dynamics for latent state due to the discrete and typically small‐dimensional nature of choice. This paper extends this literature to utilize imperfect measurements, called proxies, for a latent discrete state. I first show that…

数据丰富环境中非对称损失下的二元选择：理论与算法公平性应用AI 解读

We study the binary choice problem in a data‐rich environment with asymmetric loss functions. The econometrics literature covers nonparametric binary choice problems but does not offer computationally attractive solutions in data‐rich environments. The machine learning literature has many algorithms but is focused mostly on loss functions that are independent of covariates. We show that theoretically valid decisions on binary outcomes with general loss functions can be…

工具变量模型中的识别：Abadie 的 Kappa 的核心作用AI 解读

We study instrumental variables models characterized by: (i) Unobserved heterogeneity consisting of potential outcomes and response types that describe how the instrument determines treatment choice; (ii) Conditional independence of the instrument and the unobserved heterogeneity; and (iii) Convex restrictions on the distribution of unobserved heterogeneity. We show certain causal parameters are identified in these models if and only if a version of the kappa of Abadie…

选择由谁选择：能源返利项目中的自选择定向政策AI 解读

We develop an optimal policy assignment rule that integrates two distinctive approaches commonly used in economics—targeting by observables and targeting through self‐selection . Our method can be used with experimental or quasi‐experimental data to identify who should be treated, be untreated, and self‐select to achieve a policymaker's objective. Applying this method to a randomized controlled trial on a residential energy rebate program, we find that targeting that…

加价率估计指南：评估财务数据中的估计结果AI 解读

Macroeconomic outcomes depend on the distribution of markups across firms and over time, making firm‐level markup estimates key for macroeconomic analysis. Methods to obtain these estimates require data on the prices that firms charge. Firm‐level data with wide coverage, however, primarily come from financial statements, which lack information on prices. We use an analytical framework to show that trends in markups over time or the dispersion of markups across firms can…

含噪协变量的因果推断：异方差测量误差与差分隐私AI 解读

We study causal inference with high‐dimensional covariates, observed with various types of heteroscedastic noise. Our main assumption is that the true covariates are low rank, which we empirically evaluate with plots and interpret as approximate repeated measurements. This assumption delivers semiparametric inference on the causal parameter, as precisely as if the true covariates were available. A methodological contribution is to introduce an error‐in‐variable balancing…

逐点 CATE 估计的极小极大速率：设计平滑度的作用AI 解读

We study the role of design smoothness in the minimax estimation of conditional average treatment effects at a fixed covariate point. For design densities bounded above and away from zero, without further regularity, we derive upper and lower risk bounds with an arbitrarily small gap in the convergence-rate exponent. Under uniform design, we obtain matching upper and lower bounds at interior points in the regime of low nuisance smoothness. These results show that rough…

高维稀疏组卷积平滑分位数回归AI 解读

In many applications, covariates are naturally organized into groups, yet conventional group-selection methods often retain all variables within selected groups, failing to achieve within-group sparsity. To address this limitation, we propose an adaptive two-stage sparse-group selection procedure for high-dimensional quantile regression. The proposed method integrates convolution-smoothed quantile loss with $\ell_{2,0}$ and $\ell_0$ penalties to identify important groups and…

动态面板多项Logit模型AI 解读

This paper provides moment conditions for dynamic multinomial logit panel data models with individual specific fixed effects. The moment conditions do not depend on the fixed effects and thus form the basis for generalized method of moments estimation. For a leading case, we provide sufficient conditions under which the moment conditions identify the common parameters, and we illustrate their use in an empirical application to household soft drink purchases. The construction…

伪面板事件研究：可信应用的计量经济学基础AI 解读

Event studies with panel data are widely used to estimate treatment effects. With repeated cross-sections, future treatment dates are unobserved for untreated individuals. Pseudo-panel event studies impute pre-treatment outcomes of the treated using matched untreated individuals. This paper establishes identification in such designs and characterizes bias when identification conditions fail. I show that the pseudo-panel regression is equivalent to weighted least squares with…

理性疏忽—加性随机效用模型的估计AI 解读

This paper develops likelihood-based estimation for parametric rational inattention--additive random utility models. We establish consistency and asymptotic normality of the exact maximum likelihood estimator and provide conditions under which the simulated estimator shares its asymptotic distribution. To implement estimation, we develop Newton--BA, a KKT-guided hybrid of Newton--Raphson and Blahut--Arimoto (BA). The algorithm accommodates both interior and boundary…

TMLE 与 AIPW 中样本分割选择何时重要：交叉拟合、目标化与重复AI 解读

Variants of targeted maximum likelihood estimation (TMLE) and augmented inverse-probability weighting (AIPW) with machine-learning nuisance models differ in which observations train them, how bias is corrected, and whether splits are repeated. In known-truth simulations of a binary-outcome average treatment effect, eight headline estimators shared each setting's library, inner folds and truncation bound; coverage differences were judged against a prespecified…

当处理发生溢出：时空因果效应估计的神经网络框架AI 解读

Real-world public health policies are often enacted by local jurisdictions, but their effects can ripple through connected populations. For causal evaluation, this creates a difficult spatiotemporal problem: intermittent policy updates create path-level treatments, and because spillover depends on neighboring treatment paths along with the focal-unit's own history, controlling time-varying confounding requires high-dimensional, multimodal information from both sources. To…