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通过知识驱动的在线多模态自动表型系统降低电子健康记录研究门槛AI 解读

Electronic health records (EHRs) hold great promise for translational research but remain difficult to use at scale because diagnostic codes are noisy, disease-relevant features are hard to identify, high-quality labels are limited and patient-level data sharing is often restricted. We introduce Knowledge-driven Online Multimodal Automated Phenotyping (KOMAP), a unified system for automated, privacy-preserving EHR phenotyping using summary statistics. The system is organized…

Abstract We develop a test for instrument strength based on the bias of two-stage least squares (2SLS) that (1) generalizes Stock and Yogo’s and Sanderson and Windmeijer’s tests to be robust to heteroskedasticity and autocorrelation, and (2) extends Montiel Olea and Pflueger’s robust test for models with a single endogenous regressor to multiple endogenous regressors. Our test can be based either on an absolute bias criterion or on the 2SLS bias relative to a worst-case…

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…

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…

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…

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…

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…

Abstract Researchers using instrumental variables to investigate ordered treatments often recode treatment into an indicator for any exposure. We investigate this estimand under the assumption that the instruments shift compliers from no treatment to some but not from some treatment to more. We show that when extensive margin compliers only (EMCO) exist, this estimand captures a weighted average of treatment effects that can be partially unbundled into each complier group’s…

达成议价：工资议价冲击的叙事识别AI 解读

ABSTRACT Wage markup shocks, an important driver of business cycles, are hard to pin down. We quantify the importance of exogenous variation in wage bargaining power, one source of those shocks, from German minimum wage introduction episodes and strikes. This disciplines the impulse responses of unemployment and output, and sharpens inference for other variables, which behave consistently with predictions from search‐and‐matching models. We find that wage bargaining shocks…

贝叶斯学习中的经验贝叶斯：理解一种常见实践AI 解读

In applications of Bayesian procedures, once a class of priors has been chosen, it may be tempting to fix the prior's hyperparameters from the data, in an empirical Bayes (EB) fashion, usually by their maximum marginal likelihood estimates (MMLE). This is a quite common but questionable practice, lacking a rigorous theoretical basis. We provide a theoretical framework where this form of EB is regarded as a computational strategy for approximating a genuine Bayesian posterior…

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共同基金 alpha 的异质可预测性：稀疏聚类 GMM 方法AI 解读

Mutual fund managers' skills, measured by risk-adjusted alphas, are predictable using fund characteristics identified by machine learning (e.g., Kaniel et al., 2023, JFE; DeMiguel et al., 2023, JFE). However, alpha's predictive power varies across funds and periods, with most funds exhibiting negligible alphas. To model this heterogeneous predictability, this paper introduces Sparse Clustering GMM, a nonparametric approach for uncovering latent fund groups. SCGMM clusters…

强度变化的重复处理下的工具变量回归AI 解读

Instrumental variable models with repeated endogenous treatments are popular in empirical research using pooled cross-sectional or short panel datasets. This paper proposes a novel semi-parametric approach that explicitly considers 1) path dependency in the contemporaneous treatment effect and 2) a direct carryover effect from past treatments. We demonstrate that when either of these new features is present, the standard two-stage least squares estimator is generally…

高维协变量下局部平均处理效应的识别稳健推断AI 解读

This paper presents an inference method for the local average treatment effect (LATE) in the presence of high-dimensional covariates, regardless of the strength of identification. We propose an orthogonalized Anderson-Rubin test statistic that maintains uniformly valid asymptotic size. We provide an easy-to-implement algorithm for inferring the high-dimensional LATE by inverting our test statistic and employing the double/debiased machine learning method. Simulation results…

含固定效应的门槛空间面板回归AI 解读

We introduce general estimation and inference methods for threshold spatial panel regression with two-way fixed effects in a diminishing-threshold-effects framework. A valid objective function is obtained through a simple adjustment on the concentrated quasi loglikelihood with fixed effects being concentrated out, which leads to a consistent estimation of all common parameters. We show that the estimation of threshold parameter has a negligible effect on the asymptotic…

矩不等式模型推断的实用指南AI 解读

Models defined by moment inequalities have become a standard modeling framework for empirical economists, spreading over a wide range of fields within economics. From the point of view of an empirical researcher, the literature on inference in moment inequality models is large and complex, including multiple survey papers that document the non-standard features these models possess, the main novel concepts behind inference in these models, and the most recent developments…

资源丰富经济体的财政政策状态AI 解读

ABSTRACT We analyse fiscal policy in resource‐rich economies using a novel Bayesian regime‐switching panel model. The identified regimes capture pro‐ or countercyclical fiscal behaviour by allowing regime‐specific shifts in the average fiscal stance, while the switches between the regimes have the interpretation of changes in fiscal policy. Applying the model to a panel of sixteen oil‐producing economies, we show that fiscal policy has alternated between a procyclical and…

欧洲劳动力市场的机会性技能升级：广泛复现证据AI 解读

ABSTRACT We build on Modestino et al., who analysed “opportunistic upskilling” during the 2007 recession in the US, by extending their approach to European labour markets following recent economic shocks. Using a comprehensive dataset of online job postings (2019–2023) and an instrumental variable approach leveraging Ukrainian refugee inflows, we find that increases in labour supply have a significant positive effect on both education and experience requirements. Our…

基于共形化闭合检验的集体异常检测与计数AI 解读

This paper develops a flexible distribution-free method for collective outlier detection and enumeration, designed for situations in which the presence of outliers can be detected powerfully even though their precise identification may be challenging due to the sparsity, weakness, or elusiveness of their signals. This method builds upon recent developments in conformal inference and integrates classical ideas from other areas, including multiple testing, locally most…

非均匀高维稀疏多重超图的通用潜在嵌入建模AI 解读

Recent research has shown growing interest in modeling hypergraphs, which capture polyadic interactions among entities beyond traditional dyadic relations. However, most existing methodologies for hypergraphs face significant limitations, including their heavy reliance on uniformity restrictions for hyperlink orders and their inability to account for repeated observations of identical hyperlinks. In this work, we introduce a novel and general latent embedding approach that…

对抗性污染下的稳健回归：Welsch 估计量的理论与算法AI 解读

Convex and penalized robust regression methods often suffer from a persistent bias induced by large outliers, limiting their effectiveness in adversarial or heavy-tailed settings. In this work, we study a smooth redescending non-convex M-estimator, specifically the Welsch estimator, and show that it can eliminate this bias whenever it is statistically identifiable. We focus on high-dimensional linear regression under adversarial contamination, where a fraction of samples may…

翻转干预下的纵向加权与修剪处理效应AI 解读

Weighting and trimming are popular methods for addressing positivity violations in causal inference. While well-studied with single-timepoint data, standard methods do not easily generalize to longitudinal data and remain vulnerable to time-varying positivity violations. We extend weighting and trimming to longitudinal settings via stochastic “flip” interventions, which maintain the treatment status of subjects who would have received the target treatment, and flip others’…

干扰参数满足不等式约束时的简单自适应置信区间AI 解读

Inequalities may appear in many models. They can be as simple as assuming a parameter is nonnegative, possibly a regression coefficient or a treatment effect. This paper focuses on models with one inequality and proposes an inequality-imposed confidence interval (IICI) that has particularly attractive features. The IICI is simple in that it does not require simulations or tuning parameters. Also, the IICI is adaptive to the slackness of the inequality, uniformly valid, and…

依赖删失生存模型中回归参数的界AI 解读

Abstract We propose a semiparametric model to study the effect of covariates on the distribution of a censored event time while making minimal assumptions about the censoring mechanism. The model is partially identified, and we obtain bounds on the covariate effects which are allowed to be time-dependent. Moreover, these bounds can be interpreted as classical confidence intervals and are obtained by aggregating information in the conditional Peterson bounds over the…

Summary In cluster randomized controlled trials (CRCTs) with finite populations, the exact design-based variance of the Horvitz–Thompson (HT) estimator of the average treatment effect (ATE) depends on the joint distribution of unobserved, cluster aggregates of potential outcomes and is therefore not point-identified. We study a two-stage sampling design in which clusters are sampled at random, units are then sampled within selected clusters, and treatment is assigned at the…