每日简报

广义 Riesz 回归：基于 Bregman 散度拟合的去偏机器学习统一框架AI 解读

Estimating the Riesz representer is central to debiased machine learning, yet the generator and representer model determine which regression directions their first-order conditions protect. We introduce generalized Riesz regression, which minimizes a Bregman divergence made observable by the Riesz identity. Squared and Kullback--Leibler-type choices recover Riesz regression, tailored loss minimization, and density-ratio objectives. For any twice-differentiable generator and…

利用网络数据识别和估计半参数 Logit 模型AI 解读

This paper studies identification and estimation of a semiparametric logit model in which an unobserved individual characteristic affects both a binary outcome and the formation of social links. In this setting, the endogeneity of the network is informative rather than a nuisance: because the same latent trait drives linking behavior, observed network data can be used to control for unobserved heterogeneity in the outcome equation without imposing a parametric model of link…

一般处理类型下稳健匹配观察研究：一致性、效率与适应性AI 解读

To ensure reliable causal conclusions from observational studies, researchers routinely conduct sensitivity analysis to assess robustness to unmeasured confounding. In matched observational studies (one of the most popular observational study designs), two foundational concepts, design sensitivity and Bahadur-Rosenbaum efficiency, are used to quantify the robustness of test statistics and study designs in sensitivity analyses. Unfortunately, these measures of robustness are…

隐藏结果的近端因果推断AI 解读

Methods that rely on proxies, without imposing strong parametric structure, are increasingly used to deal with unobserved variables in causal inference. One influential line of this work reconstructs latent distributions used to identify the target functional by exploiting eigenvalue eigenvector structure. Within this framework, we first establish identification of the full data law in the presence of hidden outcomes, and then develop influence function based estimators for…

含未知趋势与周期成分的球面嵌入时间序列AI 解读

Spherically embedded time series are time series with values naturally residing on or can be equivalently mapped to the sphere. Despite their ubiquity in diverse scientific fields, these data frequently exhibit complex non-stationarity driven by latent trend and periodic components. Traditional Euclidean time series methods fail to account for the intrinsic non-Euclidean geometry of the sphere, leaving a critical gap in rigorous methodologies for modelling and forecasting…

贝叶斯指标饱和回归AI 解读

Structural break detection has emerged as an important tool for assessing the effects of policies in settings where conventional policy evaluation methods might not be applicable.In this paper, we introduce a unified Bayesian framework for detecting structural breaks with unknown timing and arbitrary sequence in longitudinal data. The proposed setup builds on a indicator-saturated regression design and uses a spike-and-slab prior for selection among indicators. We establish…

广义自回归多变量模型：从二元过程到泊松过程AI 解读

This paper presents a framework for binary autoregressive time series in which each observation is a Bernoulli variable whose success probability evolves with past outcomes and probabilities, in the spirit of GARCH-type dynamics, accommodating nonlinearities, network interactions, and cross-sectional dependence in the multivariate case. Existence and uniqueness of a stationary solution is established via a coupling argument tailored to the discontinuities inherent in binary…

通过双重差分学习说服效应AI 解读

We develop a difference-in-differences framework to measure the persuasive impact of informational treatments on behavior in staggered treatment settings. We introduce two causal parameters, the forward and backward average persuasion rates on the treated, which refine the average treatment effect on the treated. The forward rate excludes cases of "preaching to the converted," while the backward rate omits "talking to a brick wall" cases. The backward rate coincides with the…

非欧对象值时间序列的周期性量化AI 解读

Non-Euclidean object-valued time series are playing a growing role in modern data analysis, and periodicity is a fundamental characteristic of many time series. However, quantifying periodicity in general non-Euclidean random objects remains largely unexplored. In this work, we introduce a novel nonparametric framework for quantifying periodicity in random objects within a general metric space that lacks Euclidean structure. Our approach formulates periodicity estimation as…

亚组治疗效应的贝叶斯收缩方法：统一实现与比较AI 解读

Evaluating treatment effect heterogeneity across patient subgroups is a fundamental aspect of clinical trial analysis. These analyses have inherent limitations due to small sample sizes and the substantial number of subgroups investigated. There is a tendency to focus on extreme estimates, which may reflect random variation rather than true effects, potentially leading to spurious clinical conclusions. Statisticians in regulatory agencies and pharmaceutical companies have…

局部爆炸性时间序列的尾部感知密度预测：神经网络方法AI 解读

Mixed causal--noncausal (anticipative) models capture locally explosive dynamics and provide economically meaningful probabilities of continuation and collapse, but forecasting with them remains computationally difficult. We develop a two-stage framework that estimates such an ARMA model and then learns its predictive density using a Mixture Density Network with skewed-t components, tail-aware training weights, and post-hoc calibration. The approach accommodates the heavy…

加权目标量的稳健推断AI 解读

Researchers often conduct inference on weighted estimands, defined as weighted averages of group-level effects. Example settings include event studies with cohort-level effects and experiments with site-level effects. Under heterogeneous effects, different weighting schemes yield estimands with distinct empirical and policy interpretations, leading to ambiguity and disagreement over the choice of weights. I establish bounds on differences between weighted estimands and…

异质性治疗效应蒸馏：因果推断中的稳定亚组估计AI 解读

Recent methodological developments have introduced new black-box approaches to better estimate heterogeneous treatment effects; however, these methods fall short of providing interpretable characterizations of the underlying individuals who may be most at risk or benefit most from receiving the treatment, thereby limiting their practical utility. In this work, we introduce causal distillation trees (CDT) to estimate interpretable subgroups. CDT allows researchers to fit any…

利用 AI 生成测量识别缺失数据：弱影子变量方法AI 解读

Across business and social science applications, outcomes are often missing in ways that depend on the unobserved outcomes themselves. In service systems, for example, whether a customer submits a rating depends on the rating they would have provided. Such missing-not-at-random (MNAR) mechanisms make population quantities difficult to identify without strong assumptions on the observation process. Meanwhile, rich unstructured data, such as customer interaction histories, are…

存在网络溢出的交错双重差分：识别与估计AI 解读

This paper studies staggered difference-in-differences designs in which treated and comparison units can be exposed to other units' adoption. Using an exposure mapping specified by the researcher, we define realized and counterfactual exposure states and decompose the total effect into a switching effect of own adoption and a spillover effect under no own adoption. Conditional parallel trends identify the total and switching effects by matching, respectively, the…

偏斜 t 分布实现随机波动率模型：波动率与尾部风险预测AI 解读

Accurate forecasting of volatility is essential for financial risk management and for the evaluation of tail risk measures such as value-at-risk (VaR) and expected shortfall (ES). This study proposes the realized stochastic volatility (RSV) model, an extension of the traditional stochastic volatility (SV) model that incorporates realized volatility as an efficient proxy for latent volatility. To better capture the stylized features of financial return distributions,…

存在传播过程的关联群体中的因果推断AI 解读

We address a gap in the literature on causal inference in connected populations: while there is a growing body of work on estimating causal effects in connected populations, little is known about how contagion and other network processes impact causal effects and inference. Contagion and other network processes imply that the outcomes of units affect the outcomes of other units, which enables the effects of interventions to propagate throughout connected populations and…

不完整网络数据的灵活插补AI 解读

Sampled network data are widely used in empirical research because collecting complete network information is costly. However, empirical analyses based on sampled networks may lead to biased estimators. We propose a nonparametric imputation method for sampled networks and show that empirical analyses based on imputed networks yield consistent estimates. Our approach imputes missing network links by combining a projection onto covariates with a local two-way fixed-effects…

随机波动率管理组合的灵活变分近似AI 解读

This paper investigates volatility-managed portfolios through a variational Bayes approach that smooths stochastic volatility forecasts. Our algorithm exhibits competitive performance relative to established methods both in terms of inferential accuracy and computational efficiency. Analyzing equity factors and characteristic-based portfolios, we show that smoothing reduces excess leverage and turnover, significantly improving risk-adjusted returns after transaction costs.…

特征定价预算约束下的经验福利分析AI 解读

We analyze demand settings where heterogeneous consumers maximize utility for product attributes subject to a nonlinear budget constraint. We develop nonparametric methods for welfare-analysis of interventions that change the constraint. Two new findings are Roy's identity for smooth, nonlinear budgets, which yields a Partial Differential Equation system, and a Slutsky-like symmetry condition for demand. Under scalar unobserved heterogeneity and single-crossing preferences,…

基于数据依赖分区的条件分布设定检验AI 解读

This article introduces a Pearson-type goodness-of-fit test for the parametric specification of conditional distribution models with continuous responses. Under correct specification, the Rosenblatt transform is uniformly distributed on $[0,1]$ conditionally on the explanatory variables. The test exploits this characterization by cross-classifying the transformed observations and the explanatory variables according to partitions of $[0,1]$ and their support, respectively.…

条件异方差时间序列模型参数变化的稳健检验AI 解读

Structural changes and outliers often coexist, complicating statistical inference. This paper addresses the problem of testing for parameter changes in conditionally heteroscedastic time series models, particularly in the presence of outliers. To mitigate the impact of outliers, we introduce a two-step procedure comprising robust estimation and residual truncation. Based on this procedure, we propose a residual-based robust CUSUM test and its self-normalized counterpart. We…

双框抽样框架下的半参数有效数据整合AI 解读

Integrating probability and non-probability samples is increasingly important, yet unknown non-probability inclusion mechanisms complicate identification and efficient estimation. We develop semiparametric theory for dual-frame data integration and propose two complementary estimators. The first models the conditional inclusion probability parametrically and, under correct specification, attains the semiparametric efficiency bound. The probability sample serves as an…

主权评级迁移计数的边际持久性与动态 Copula 依赖：离散区间似然 MAGMAR 分析AI 解读

This paper develops an observed-data likelihood for applying moving-aggregate modified autoregressive (MAGMAR) copula time-series models to discrete sovereign rating-migration counts with time-varying exposure. An annual count identifies a probability-integral-transform interval rather than a unique latent point, so the likelihood integrates the latent process over the complete sequence of count intervals. A guided sequential Monte Carlo implementation incorporates the…