每日简报

稳定化高阶影响函数：一类双线性形式的统计理论AI 解读

Higher-order influence functions, introduced in a series of articles (Robins et al., 2008, 2009a; van der Vaart, 2014; Robins et al., 2016, 2023; Liu et al., 2017), are a unified framework for constructing rate-optimal point estimates of a class of statistical functionals under various complexity-reducing assumptions on the posited statistical model that generates the observed data. Although higher-order (influence functions) estimators are theoretically appealing, they have…

高维稀疏情境信息下组合优化器的学习后推断：最小方向扰动方法AI 解读

We study post-learning inference for structural properties of data-dependent combinatorial optimizers. The target is whether an oracle optimizer, rather than a latent parameter or smooth functional, belongs to a prescribed class, such as a category-mix, inventory, or resource-feasibility class. We focus on a high-dimensional contextual multinomial logit model with sequentially adaptive data collection, where the parameter-to-optimizer map is discontinuous and the policy…

基于P样条的平滑降秩回归AI 解读

Linear regression is one of the core statistical tools used for analysis of data. In the era of statistical learning, linear regression has been expanded into two directions. The first is regularisation, where penalties are added to the loss function to obtain more stable or sparse solutions. The second direction is basis expansion, such as with spline or kernel functions, where the linearity assumption is dropped. In practice, empirical researchers often collect multiple…

高维下广义回归估计量的方差估计AI 解读

In survey sampling, the goal is to estimate finite population parameters such as totals, means, and proportions. At the estimation stage, it is common to have access to auxiliary information in the form of covariates known either in aggregate form or for each population unit. These covariates are often used, through models relating them to the variable of interest, to improve efficiency; this approach is known as model-assisted estimation. Modern applications increasingly…

因果重叠效应：累积固定效应方法AI 解读

Social scientists often ask about the effect of increasing one's duration of exposure to a social context on one's outcomes, i.e. the overlap effect. Past studies adopted a unidimensional treatment effect framework to estimate the effect of overlap, imposing important restrictions. In this paper, we propose a new causal framework of multidimensional treatments where the overlap effects include both the duration and the content of overlap, under which, for instance, the…

函数自回归模型中的极小极大预测AI 解读

The Functional Autoregressive Model (FAR) generalizes the multivariate AR(1) model in Time Series Analysis to functional data. It serves as a historical foundational point in the study of functional time series and remains a fundamental and widely used model for dependent functional data. The process-the observed data-generated by the FAR model forms a Hilbert-valued Markov chain. This paper investigates the non-asymptotic prediction mean square error and derives a lower…

利用边际矩信息识别并界定因果效应的中心矩AI 解读

Evaluating the causal effect of a treatment on an outcome is a central objective in causal inference. While the average causal effect summarizes the mean impact of treatment, the central moments of the individual causal effect (ICE) characterize the shape of the ICE distribution, thereby revealing the extent and structure of treatment effect heterogeneity across individuals. This paper investigates the identification and bounding of the central moments of the ICE using only…

饱和固定效应模型的方差估计AI 解读

We characterize the asymptotic behavior of conventional variance estimators in linear regression with high-dimensional fixed effects under a drift in which both the proportional fixed-effect dimension $ρ_n = d_{K_n}/n \to ρ\in [0,1)$ and the residual treatment variance $τ_n^2 = nQ_{K_n} \to τ^2 \in (0, \infty]$ are non-degenerate. Three findings emerge. First, under strict exogeneity and conditional homoskedasticity, the Cattaneo--Jansson--Newey-corrected $t$-statistic is…

时间序列基础模型预测已实现波动率：与计量基准的比较AI 解读

We ask whether pretrained time series foundation models (TSFMs) improve on established econometric benchmarks for forecasting realized volatility. Using the VOLARE dataset, we conduct the first systematic comparison of nine zero-shot TSFMs against eight econometric specifications, including the Heterogeneous Autoregressive (HAR) family, across 50 assets in equities, foreign exchange, and futures, and three forecast horizons, with formal pairwise and multi-model…

复发事件结局的双重稳健方法：降压治疗对急性肾损伤的因果效应AI 解读

Evaluating the average causal effects of treatment strategies on recurrent event outcomes, such as heart attacks or renal failure, is important in clinical and medical research. However, the analysis becomes increasingly complex as multiple interacting factors are considered within a longitudinal setting. In this paper, we use advanced methodologies to estimate the average causal effects of standard versus intensive blood pressure-lowering therapies on acute kidney injury…

用随机镜像上升近似极小极大估计有界正态均值AI 解读

This paper presents a computational approach to find an approximately minimax estimator for the classical Bounded Normal Mean problem. The suggested procedure is the Bayes estimator corresponding to an approximately least-favorable distribution obtained from a stochastic mirror ascent routine for concave maximization. The paper shows that both the approximately least-favorable distribution and the approximately minimax estimator are indeed close (in a sense we make precise)…

政权更替的实证全局博弈模型AI 解读

Global games theory provides a tractable framework for analyzing coordination problems with multiple equilibria, with regime overthrow serving as a canonical application. A large empirical literature on coups d'état examines the relationship between country-level characteristics, coup occurrence, and coup success using reduced-form approaches that leave the underlying coordination problem implicit. Bridging these literatures, we develop an estimable global games model of…

营销干预中贝叶斯因果影响模型的敏感性与早期检测AI 解读

Marketing systems frequently undergo operational changes that may affect performance, making timely detection of adverse effects essential for decision making. While Bayesian Causal Impact models are widely used to estimate the causal effects of interventions, their ability to support early operational monitoring remains less explored. This paper proposes a simulation based framework to evaluate the sensitivity and detection capabilities of Bayesian causal impact analysis…

谱密度矩阵的多元贝叶斯 P 样条估计及 LISA TDI 噪声应用AI 解读

We present a Bayesian P-spline method for estimating the frequency-dependent cross-spectral density matrix of stationary multivariate time series. The inverse spectral matrix is parametrised through its frequency-varying Cholesky decomposition, which guarantees Hermitian positive definiteness at every frequency. Each real log-diagonal entry and each real and imaginary off-diagonal entry is given an independent penalised B-spline prior that controls smoothness. Inference uses…

学会到哪里寻找数据：用于政策选择与数据收集的 Delaunay 匹配AI 解读

This paper studies data-driven policy choices from a geometric perspective. A sample from a donor population fully exposed to an innovation informs a policymaker (PM) on whether to innovate groups in a target population where the innovation was not introduced. Any wrong decision must be compensated from a finite budget and the PM seeks an estimator of the innovation's effect that guarantees an affordable compensation cost. I focus on matching estimators with positive weights…

干扰下不依赖暴露映射的通用因果推断框架AI 解读

We develop a general framework for design-based causal inference under interference in cluster experiments conducted via two-stage randomization on a network of interconnected units, without relying on exposure mapping assumptions, exclusion of cross-cluster interference, or Bernoulli treatment assignments. Within this framework, we establish a complete characterization of linear weighted estimators (LW) as defined by Godambe (1955) that achieve identification of various…

We propose a structural projection (SP) method for identifying and estimating structural parameters in regression models associated with endogenous regressors. The method projects the structural error onto the sigma-field generated by the regressors, decomposing it into a conditional mean component and an orthogonal residual. This decomposition yields an internally constructed instrument that is valid whenever the conditional mean lies in a known subspace, avoiding the need…

安慰剂断点设计AI 解读

Standard regression discontinuity design (RDD) models rely on the continuity of expected potential outcomes at the cutoff. The standard continuity assumption can be violated by strategic manipulation of the running variable, which is realistic when the cutoff is widely known and when the treatment of interest is a social program or government benefit. In this work, we identify the treatment effect despite such a violation, by leveraging a placebo treatment and a placebo…

无限维部分识别参数的一致置信带AI 解读

Infinite-dimensional parameters are ubiquitous in empirical economics. This paper develops an Imbens--Manski--Stoye type confidence band for infinite-dimensional partially identified parameters. In particular, we propose multiplier bootstrap-based construction of a uniform confidence band. By employing approximation theorems for suprema of non-centered empirical processes indexed by possibly non-Donsker classes \citep{chernozhukov2016empirical}, we confirm the uniform…

杠杆效应与波动率之波动的经验特征函数估计及可行推断AI 解读

We develop jump-robust estimators of the leverage effect and volatility of volatility using high-frequency data. Our construction begins with a spot volatility estimator based on the empirical characteristic function of high-frequency increments. This method can mitigate the contamination from jumps, which can be of infinite variation. We then construct estimators of the leverage effect and volatility of volatility and correct for the bias induced by spot volatility…

极端事件中的格兰杰因果关系AI 解读

We introduce a rigorous mathematical framework for Granger causality in extremes, designed to identify causal links from extreme events in time series. Granger causality plays a pivotal role in uncovering directional relationships among time-varying variables. While this notion gains heightened importance during extreme and highly volatile periods, state-of-the-art methods primarily focus on causality within the body of the distribution, often overlooking causal mechanisms…

线性回归中的潜在权重与隐含因果设计AI 解读

Applied researchers routinely use linear regression to estimate causal effects, justified by quasi-experimental treatment variation, while leaving assumptions on treatment assignment implicit. We formalize a minimal criterion for quasi-experimental interpretation -- that the regression estimates some contrast of potential outcomes under the true assignment process, regardless of potential outcomes -- and characterize its implications for arbitrary regressions. This criterion…

均值需估计时 L-mixing 时间序列经典谱估计量的非渐近分析AI 解读

Spectral estimation is an important tool in time series analysis, with applications including economics, astronomy, and climatology. The asymptotic theory for non-parametric estimation is well-known but the development of non-asymptotic theory is still ongoing. Our recent work obtained the first non-asymptotic error bounds on the Bartlett and Welch methods with restrictive assumptions. In this work, we derive non-asymptotic error bounds for both Bartlett and Welch estimators…

函数型时间序列共同趋势的统计推断AI 解读

We study statistical inference on unit roots and cointegration for time series in a Hilbert space. We develop statistical inference on the number of common stochastic trends embedded in the time series, i.e., the dimension of the nonstationary subspace. We also consider tests of hypotheses on the nonstationary and stationary subspaces themselves. The Hilbert space can be of an arbitrarily large dimension, and our methods remain asymptotically valid even when the time series…