A variance bound for unbiased estimation in inverse sampling

In this paper, we develop bootstrap methods for factor-augmented regressions with potentially weak common components, allowing signal eigenvalues to diverge at heterogeneous rates $N^{α_k}$, where $0<α_k\leq1$, $k=1,\ldots,r$. The regression estimator targets a rotation of the latent parameter vector that may be data dependent, while factor estimation can induce asymptotic bias. The proposed procedure directly constructs the rotation-specific target in the bootstrap world,…

金融 · 金融因子 Bootstrap 与重抽样金融 · 收益预测

关联记录被引 0 次 ↗OpenAlex · 核验 2026-10-01

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Testing Conditional Distribution Equality Using Generative Models

Zekai Jin · Yi Zhang · Yun Yang · Xiaofeng Shao首次提交 2026-10-14 · 修订 2026-10-14 · v1arXiv预印本

使用生成模型检验条件分布相等AI 解读

Conditional distribution equality testing is a core problem with applications in causal inference, model diagnostics, and transfer learning. Existing methods often lose power when the conditioning variable is high-dimensional. We propose a new test that leverages generative models to compare conditional distributions using both observed data and synthetic samples. A sample-splitting and studentization scheme is introduced to control estimation error. Under the null, the test…

微观 · 因果推断高维与机器学习核、筛与局部多项式

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Propensity score with factor loadings: evaluating the effect of the Paris Agreement on the returns of European stocks

Angelo Forino · Andrea Mercatanti · Giacomo Morelli首次提交 2025-07-11 · 修订 2026-10-14 · v2arXiv预印本

Factor models for longitudinal data, where policy adoption is unconfounded with respect to a low-dimensional set of latent factor loadings, have become increasingly popular for causal inference. Most existing approaches, however, rely on a causal finite-sample approach or computationally intensive methods, limiting their applicability and external validity. In this paper, we propose a novel method for panel causal inference within a super-population framework, based on…

微观 · 因果推断微观 · 面板数据金融 · 金融因子

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Estimating Causal Treatment Effects in Placebo-Controlled Randomized Clinical Trials When High Placebo Response is Anticipated

Yang Song · Yunzhe Qian · Chanmin Kim · Gheorghe Doros首次提交 2026-09-08 · 修订 2026-10-14 · v2arXiv预印本

预期高安慰剂反应时安慰剂对照随机试验的因果效应估计AI 解读

In placebo-controlled randomized clinical trials (RCTs), the placebo response significantly modifies treatment effects and diminishes the intention-to-treat (ITT) treatment effect, $Δ_{ITT}$. This study presents a novel two-stage framework for estimating the standardized causal treatment effect, $Δ_{STD}$, among the ITT population, under the assumption that their placebo responses are similar to the levels of self-administering medication at home. The first stage employs a…

微观 · 因果推断

关联记录被引 0 次 ↗OpenAlex · 核验 2026-10-01

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Two Gaussians, Too Many: A bootstrap-based approach to assess identifiability in non-Gaussian structural vector autoregressions

Paritosh Shankarrao Junare首次提交 2026-07-19 · 修订 2026-10-14 · v2arXiv预印本

Normality pre-tests on reduced-form innovations cannot detect whether two or more underlying structural shocks are Gaussian, and hence cannot detect a failure of identification in non-Gaussian SVARs. We propose a bootstrap-based approach that reveals such a failure. Under the null of valid identification and certain regularity conditions, the conditional bootstrap distribution of the maximum likelihood estimator of the impact matrix is asymptotically normal, so the…

宏观 · 时间序列 Bootstrap 与重抽样极大与准极大似然

关联记录被引 0 次 ↗OpenAlex · 核验 2026-10-01

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Associating High-Dimensional Longitudinal Datasets through an Efficient Cross-Covariance Decomposition

Jianbin Tan · Pixu Shi首次提交 2026-01-19 · 修订 2026-10-14 · v2arXiv预印本

Understanding associations between paired high-dimensional longitudinal datasets is a fundamental yet challenging problem arising across scientific domains, including longitudinal multi-omic studies. The difficulty stems from the complex, time-varying cross-covariance structure coupled with high dimensionality, which complicates both model formulation and statistical estimation for association discovery. To address these challenges, we propose a new framework called…

面板数据结构变化与时变参数正则化与变量选择

关联记录被引 0 次 ↗OpenAlex · 核验 2026-10-15

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