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失业、失业保险与健康：来自巴西的证据AI 解读

Abstract We study the health impacts of job loss and unemployment insurance (UI) among Brazilian workers, using unique administrative data linking employment, hospitalizations, health insurance, and mortality records over 17 years. Leveraging mass layoffs for identification, we find that job loss raises hospitalization (+33%) and mortality (+23%) risks for men, with no significant effects for women. Impacts are pervasive across age, income, and education levels, with…

重返办公室政策与员工任职年限分布AI 解读

Abstract Debates over return-to-office mandates have intensified since the COVID-19 pandemic ended, though their economic implications remain poorly understood. Using large-scale resume data, we analyze how these policies affect employee tenure and seniority at three large U.S. tech companies: Microsoft, SpaceX, and Apple. Employing a distributional synthetic controls framework, we estimate a reduction in tenure and seniority at firms returning to the office. We extend the…

均衡扩散模型中从微观到宏观的推断AI 解读

Abstract We quantify the benefits of better firm-to-firm matching in an aggregate diffusion model where individuals acquire profitable knowledge from others. We estimate the model using a small-scale program in Kenya that creates new opportunities for firm managers to interact. Aggregate gains from the program depend on the relative importance of meeting a high-knowledge firm versus learning within that meeting. We show how moments from the intervention identify these…

极端事件估计的分布鲁棒优化框架AI 解读

Conventional methods for extreme event estimation rely on well-chosen parametric models asymptotically justified from extreme value theory (EVT). These methods, while powerful and theoretically grounded, could however encounter a difficult bias-variance tradeoff that exacerbates especially when data size is too small, deteriorating the reliability of the tail estimation. In this paper, we study a framework based on the recently surging literature of distributionally robust…

异质分布式隐私约束下非参数回归的最优联邦学习AI 解读

This paper investigates federated learning for nonparametric regression under heterogeneous differential privacy (DP) constraints. Data is distributed across multiple servers with varying sample sizes and privacy budgets, with each server communicating privatized summaries to a central aggregator. Our work operates within the Federated Differential Privacy (FDP) framework, which generalizes both local and central DP frameworks.We establish the minimax optimal rates of…

高维因子模型中多个结构断点的分类与估计AI 解读

This paper proposes a quasi-maximum likelihood (QML) estimator for break points in high-dimensional factor models, specifically accounting for multiple structural breaks. We begin by establishing a necessary and sufficient condition to categorize two distinct types of breaks in factor loadings: singular changes and rotational changes. The analysis of the nearly singular subsample covariance matrices of the pseudo-factors plays a key role in our approach. It allows us to…

结合源分布混合的引导式对抗鲁棒迁移学习AI 解读

Transfer learning is a critical technique that enables the application of knowledge gained from existing tasks or domains to improve performance on a new one, reducing the need for extensive data and training in each new context. Many existing transfer learning methods rely on leveraging information from source populations closely resembling the target population. However, this approach often overlooks valuable knowledge that may be present in different yet potentially…

分层空间上的非参数统计及其在对象数据分析中的应用AI 解读

Victor Patrangenaru and Daniel E. Osborne. Boca Raton, FL: Chapman & Hall/CRC Press, 2025, xv + 187 pp., $189.99(H), ISBN: 978-1-138-04313-8.Nowadays, more and more data we meet are not just simple...

异质数据整合的表征检索学习AI 解读

In the era of big data, large-scale, multi-source, multi-modality datasets are increasingly ubiquitous, offering unprecedented opportunities for predictive modeling and scientific discovery. However, these datasets often exhibit complex heterogeneity, such as covariates shift, posterior drift, and blockwise missingness, which worsen predictive performance of existing supervised learning algorithms. To address these challenges simultaneously, we propose a novel Representation…

基于二次型的再随机化统一框架AI 解读

When designing a randomized experiment, one way to ensure treatment and control groups exhibit similar covariate distributions is to randomize treatment until some prespecified level of covariate balance is satisfied; this strategy is known as rerandomization. Most rerandomization methods utilize balance metrics based on a quadratic form vTAv, where v is a vector of covariate mean differences and A is a positive semi-definite matrix. In this work, we derive general results…

可观测因子与潜在因子AI 解读

We test for temporal stability in local linear projection coefficients of observable factors on latent ones embedded in a high-dimensional vector obeying a linear factor model. The proposed test explores the fact that, under the null hypothesis, residuals from global linear projections of observable factors on latent ones, computed over a fixed time interval via Principal Component Analysis (PCA), should also be locally uncorrelated with the PCA factors. The test is fully…

贝叶斯时空变化边界识别AI 解读

Stochastic process models for spatiotemporal data underlying random fields find substantial utility in a range of scientific disciplines. Subsequent to predictive inference on the values of the random field (or spatial surface indexed continuously over time) at arbitrary space-time coordinates, scientific interest often turns to gleaning information regarding zones of rapid spatial-temporal change. We develop Bayesian modeling and inference for directional rates of change…

Chatterjee 图相关系数的偏差修正AI 解读

Summary Azadkia & Chatterjee (2021) recently introduced a simple nearest-neighbour graph-based correlation coefficient that consistently detects both independence and functional dependence, in both unconditional and conditional settings. Specifically, it approximates a measure of dependence that equals 0 if and only if the variables are (conditionally) independent, and 1 if and only if they are (conditionally) functionally dependent. However, this nearest-neighbour estimator…

异质隐私约束下函数均值估计的最优联邦学习AI 解读

Abstract Federated learning (FL) is a distributed machine learning technique designed to preserve data privacy and security, and it has gained significant importance due to its broad range of applications. This paper addresses the problem of optimal functional mean estimation from discretely sampled data in a federated setting. We consider a heterogeneous framework where the number of individuals, measurements per individual, and privacy parameters vary across one or more…

设定 LASSO 与灵活的特征型资产定价模型AI 解读

This paper studies a partially linear semiparametric additive model that allows for interactions among variables in high-dimensional settings. We propose the Specification LASSO (S-LASSO), a two-step method that integrates LASSO and Adaptive Group LASSO to simultaneously perform variable selection and model specification. Theoretically, we establish that S-LASSO possesses oracle properties in terms of selection consistency and asymptotic normality. Monte Carlo simulations…

用多元分数布朗运动建模与预测已实现波动率AI 解读

A multivariate fractional Brownian motion (mfBm) with component-wise Hurst exponents is used to model and forecast realized volatility (RV). We investigate the interplay between correlation coefficients and Hurst exponents and propose a novel method to estimate model parameters, establishing its consistency and asymptotic normality. Additionally, we develop a time-reversibility test, which is typically not rejected by RV data. When the data generating process is a…

时间序列偏度、峰度与正态性的自归一化检验AI 解读

Testing for skewness, kurtosis, and normality for time series data is highly relevant for modeling and testing purposes in econometrics and statistics. It also affects our understanding of many economic and financial phenomena and the validity of the models developed to explain them. In this paper, we propose self-normalized tests for skewness, kurtosis, and normality that can eliminate the effect of the long-run variance (LRV). In particular, our tests allow us to avoid…

双向固定效应回归估计了什么？数值等价关系的启示AI 解读

Summary This paper develops numerical and causal interpretations of two-way fixed effects (TWFE) regressions in settings with nonbinary, nonstaggered treatments and time-varying covariates. Using the equivalence between TWFE and pooled first-difference (FD) regressions, I express the TWFE coefficient as a weighted average of FD coefficients across all horizons, clarifying how short- and long-run changes contribute to the estimate. Causal interpretation of the TWFE…

断点差分设计中的分位数处理效应AI 解读

Summary This paper studies identification and doubly robust (DR) estimation of quantile treatment effects (QTEs) in difference-in-discontinuities (diff-in-disc) designs when a new treatment is introduced in the post period and a confounding discontinuous policy is present in both periods. We show that QTEs are point identified under a conditional stable-distributional-effect assumption for the confounding treatment, a restriction that is analogous to the distributional…

用于企业生存预测的高维删失 MIDAS 逻辑回归AI 解读

This paper addresses the challenge of forecasting corporate distress, a problem marked by three key statistical hurdles: (i) right censoring, (ii) high-dimensional predictors, and (iii) mixed-frequency data. To overcome these complexities, we introduce a novel high-dimensional censored MIDAS (Mixed Data Sampling) logistic regression. Our approach handles censoring through inverse probability weighting and achieves accurate estimation with numerous mixed-frequency predictors…

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非线性模型中缺失协变量的插补AI 解读

ABSTRACT I propose a new imputation estimator for missing covariate values in nonlinear models. The estimator provides efficiency gains relative to just using the complete cases, and is consistent for any model that falls under M‐estimation. This is unlike the commonly used dummy variable method and regression imputation, which I show to be generally inconsistent in nonlinear models. The proposed estimator is straightforward to implement, and relies only on the commonly used…

存在干扰参数时矩约束的统一推断AI 解读

This article proposes a simple unified inference approach on moment restrictions in the presence of nuisance parameters. The proposed test is constructed based on a new characterization that avoids the estimation of nuisance parameters and can be broadly applied across diverse settings. Under suitable conditions, the test is shown to be asymptotically size controlled and consistent for both independent and dependent samples. Monte Carlo simulations show that the test…

异方差稳健检验统计量尺寸可控的充要条件AI 解读

We revisit size controllability results in Pötscher and Preinerstorfer (2025, Econometric Theory 41, 249–301) concerning heteroskedasticity robust test statistics in regression models. For the special, but important, case of testing a single restriction (e.g., a zero restriction on a single coefficient), we provide a necessary and sufficient condition for size controllability, whereas the condition in Pötscher and Preinerstorfer (2025, Econometric Theory 41, 249–301) is, in…

自来水与下水道的价值：19世纪芝加哥的证据AI 解读

Abstract We estimate the impact of piped water and sewers on property values in late 19th century Chicago. The cost of sewer construction depends sensitively on imperceptible variation in elevation, and such variation delays water and sewer service to part of the city. This delay provides quasirandom variation for causal estimates. We extrapolate ate estimates from our natural experiment to the area treated with water and sewer service during 1874–1880 using a new estimator.…