论文检索

欧元区货币政策传导是否存在不对称性？AI 解读

We answer the question posed in the title with a nonlinear mixed-frequency vector autoregression, estimated with Bayesian additive regression trees. The model combines monthly macro-financial variables with quarterly bank lending survey data, and identifies the dynamic responses from high-frequency policy surprises. Sign asymmetry dominates; a tightening produces monetary policy transmission mostly in line with the theoretical predictions, whereas easing of any size produces…

识别临床AI预测多重性中亚组脆弱性的统计框架AI 解读

AI models trained on the same data can disagree about patient risk, with disagreement potentially concentrated in clinically important subgroups. We propose V(S), a statistically grounded vulnerability index combining an observable lower-bound witness of model disagreement with clinical severity, and develop inference and multiplicity-adjustment procedures for auditing prespecified subgroups. We applied the framework to two large critical-care cohorts, MIMIC-IV (n = 65,078)…

谁驱动系统？根据轨迹数据分类平均场粒子系统AI 解读

We study the supervised classification problem for interacting particle systems (IPS) from trajectory observations. The IPS belongs to one of K classes, each characterized by a distinct interaction drift. Given a learning dataset, the goal is then to predict the class label of a newly observed system based on the trajectory of a single particle. This setting raises several statistical challenges. First, the particles within each system interact and are therefore dependent.…

BRP-GABLE：适用于多种结果类型的可解释规则预测框架AI 解读

Interpretable prediction models are important in biomedical research, where predictive accuracy must often be balanced against the ability to examine predictor-outcome relationships. Automatic Binary Logistic Estimation (ABLE) provides interpretable rule-based representations by constructing additive models using threshold rules. However, the original method is restricted to binary outcomes, and its greedy forward construction follows a search path, making candidate rules…

删失持续时间模型中的未观测异质性检验：EM方法AI 解读

Ignoring unobserved heterogeneity in duration models biases parameter estimates and invalidates inference, but testing for it is non-regular: the null hypothesis lies on the boundary of the parameter space and some parameters are unidentified under the null. These features render standard asymptotic theory inapplicable. This paper develops an EM test for unobserved heterogeneity in censored Weibull duration models, building on the EM approach of Li, Chen, and Marriott…

有序分布变化与保守转移基准：测度、识别与推断AI 解读

Repeated cross-sections reveal changes in ordinal distributions but not the transitions producing them. I axiomatically characterize a threshold-weighted probability metric for ordinal change from threshold-crossing principles. For any threshold-additive ordinal geometry, the discrepancy coincides with the Wasserstein--1 distance induced by that ground metric and measures minimum displacement; its optimizing plans define conservative transition benchmarks. With missing…

交替处理实验中的随机化检验AI 解读

Switchback experiments assign an experimental unit, such as a market or a platform, to treatment or control over successive blocks of time. Inference can be challenging because these experiments often contain only a small number of randomized blocks, while outcomes may exhibit serial dependence, seasonality, and treatment effects that persist across periods. We develop a conditional randomization test for the null of no total treatment effect that is finite-sample valid…

一般矩变点的最优检测：均值与协方差变点同时检测及推广AI 解读

We study multiple change-point detection in multivariate time series whose distributions change in a piecewise constant manner. Distributional changes can manifest across different moment orders, from shifts in the mean and covariance to changes in higher-order moments. Higher-order moments capture increasingly rich distributional features but become difficult to estimate in high dimensions. Our tensor representation unifies moments of different orders within a common linear…

本地收益大、净收益小：波多黎各首次行业进入的直接与溢出效应AI 解读

I study how first sizable industry entries reshape local and neighboring labor markets in Puerto Rico. Using over a decade of quarterly municipality--industry data (2014Q1--2025Q1), I identify ``first sizable entries'' as large, persistent jumps in establishments, covered employment, and wage bill, and treat these as shocks to local industry presence at the municipio--industry level. Methodologically, I combine staggered-adoption difference-in-differences estimators that are…

工具变量回归中维度自适应的自助法 Anderson–Rubin 检验AI 解读

Weak-identification-robust tests for instrumental variable (IV) regressions are typically developed separately depending on whether the number of IVs is treated as fixed or increasing with the sample size, forcing researchers to make a stance on the asymptotic behavior, which is often ambiguous in practice. This paper proposes a bootstrap-based, dimension-agnostic Anderson-Rubin (AR) test that achieves correct asymptotic size regardless of whether the number of IVs is fixed…

线性IV模型中持续更新GMM的多项式方法AI 解读

This paper characterizes the global minimum of the continuously updating generalized method of moments (CU-GMM) objective in linear instrumental variables models. We allow optimal weighting matrices under heteroskedasticity, autocorrelation, or clustering. We show that the objective is a ratio of polynomials. For one endogenous regressor, stationary points of CU-GMM objective function are real eigenvalues of a companion matrix. Comparing their objective values with the value…

LOCO-AdaMP：兼顾预测与内置变量重要性推断的自适应小块集成AI 解读

As black-box machine learning models become increasingly common, extracting interpretations with uncertainty quantification has become a critical challenge. One popular type of interpretation is leave-one-covariate-out (LOCO) feature importance, while prior LOCO inference methods often require data-splitting or model-refitting. A recent ensemble framework, LOCO-MP, addresses these challenges using minipatches that subsample both observations and features, but massive feature…

概率季节性调整AI 解读

Seasonal adjustment is fundamental to economic analysis, but uncertain because seasonal components are inherently latent. This article introduces a probabilistic model discovery method that decomposes a time series into seasonal and nonseasonal components. The method returns a posterior distribution over the structure and parameters of a seasonal component. In simulation studies, the method can improve point forecasts, interval predictions, and recovery of seasonal…

处理是否有益？有序结局因果推断的贝叶斯方法AI 解读

Randomized experiments with ordinal outcomes are common across scientific applications, but conventional causal estimands such as the average treatment effect are difficult to interpret because ordinal categories lack meaningful numerical spacing. Two interpretable estimands, the probabilities that the treatment is beneficial and strictly beneficial, depend on the joint distribution of the potential outcomes and are therefore not identifiable from observed data. The sharp…

多维聚类依赖下无需交叉拟合的去偏机器学习AI 解读

This paper develops an asymptotic theory for two-step debiased machine learning (DML) estimators in generalised method of moments (GMM) models with general multiway clustered dependence, without relying on cross-fitting. While cross-fitting is commonly employed, it can be statistically inefficient and computationally burdensome when first-stage learners are complex and the effective sample size is governed by the number of independent clusters. We show that valid inference…

图分割贝叶斯因果森林：空间异质性处理效应估计AI 解读

In spatial observational studies, treatment assignment and outcomes often exhibit spatial dependence patterns, and treatment effects may vary across space and subpopulations due to both measured and unmeasured spatially structured confounders. Accounting for spatial dependence while estimating heterogeneous treatment effects (HTEs) is a central task in spatial causal inference. Causal Bayesian additive regression tree methods are popular nonparametric methods for modeling…

潜在结果变量因果效应的非参数识别与估计AI 解读

How should researchers conduct causal inference when the outcome of interest is latent and measured imperfectly by multiple indicators? We develop a general nonparametric framework for identifying and estimating average treatment effects on latent outcomes in randomized experiments. We show that latent-outcome estimation faces a fundamental but overlooked noncomparability problem due to measurement-set dependence: adding, removing, or replacing measurement targets a…

大面板中的简洁、无需特定参数设定的因果推断AI 解读

We propose a simple and transparent approach to causal inference using panel data with a large number of units and periods. Instead of imposing a particular parametric specification on the untreated potential outcomes, we assume they follow a nonparametric nonseparable factor model. We use long pre-treatment outcome histories to construct a measure of similarity between the latent factors of different units. Using this measure, we impute missing counterfactual means and…

BGM-IV：面向高维协变量工具变量回归的贝叶斯生成建模AI 解读

Instrumental-variable (IV) regression enables causal estimation under endogeneity, but modern IV problems often involve nonlinear structural effects and high-dimensional covariates. Existing methods typically operate in observed or generic learned feature spaces, and they often yield point estimates without uncertainty quantification. We introduce BGM-IV, a Bayesian generative modeling approach that performs nonlinear IV regression through posterior inference in a causally…

核设定检验的数据驱动调参：选择渐近理论、有效性与功效AI 解读

We develop model checks for conditional moment restrictions whose tuning parameters, the kernel bandwidth and the regularization level, are selected by cross-validation. The tests regress the estimated residuals on the covariates via kernel ridge regression (KRR), yielding a coefficient function in a reproducing kernel Hilbert space (RKHS) that vanishes under the null and is nonzero for any misspecification not annihilated by the Neyman projection. Our central contribution…

多元结果非可分工具变量模型的识别与估计AI 解读

This paper proposes a novel nonseparable instrumental variable model to address endogeneity in settings with multivariate outcomes. While traditional approaches typically estimate separate one-dimensional quantile models for each outcome, the inherent correlation among different dimensions frequently invalidates their fundamental assumptions. To overcome this limitation, our framework jointly models the distribution of multiple outcomes. By assuming that the structural…

欧盟价格稳定：基于随机矩阵理论的系统分析AI 解读

Price stability remains a pillar in monetary policy practices and carries a special importance within monetary unions. Mainstream economics tried to leverage price stability using price indices and several metrics to shed light on specific dynamics and optimal macroeconomic levels. The wide availability of data led researchers to consider the study of systems using Random Matrix Theory, based on inner correlation patterns. This aims to enhance the multivariate analysis by…

离散时间滞后鞅差阵列的稳定中心极限定理及动态因果应用AI 解读

Recent work in dynamic causal inference introduced a class of discrete-time stochastic processes that generalize martingale difference sequences and arrays as follows: the random variates in each sequence have expectation zero given certain lagged filtrations but not given the natural filtration. We formalize this class of stochastic processes and prove stable central limit theorems (CLTs) via martingale-coboundary decomposition, leveraging the classical martingale CLT. We…

双重差分与合成控制的结合：双重稳健识别和估计AI 解读

Difference-in-Differences (DiD) and Synthetic Control (SC) are widely used methods for causal inference in panel data, each with distinct strengths and limitations. We propose a novel method for short-panel causal inference that integrates the advantages of both approaches. Our method delivers a doubly robust identification strategy for the average treatment effect on the treated (ATT) under either of two non-nested assumptions: parallel trends or a group-level SC condition.…