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高度分层实验的估计效率AI 解读

This paper studies the use of highly stratified designs for the efficient estimation of a large class of treatment effect parameters that arise in the analysis of experiments. By a “highly stratified” design, we mean experiments in which units are divided into blocks of a fixed size and a proportion within each block is assigned to a binary treatment uniformly at random. The class of parameters considered are those that can be expressed as the solution to a set of moment…

具有二元分位数选择的分位数回归半参数估计AI 解读

This article proposes a novel method for estimating quantile regression models that account for sample selection. Unlike the approach by Arellano and Bonhomme (2017, Econometrica 85(1), 1–28; hereafter referred to as AB17), which employs a parametric selection equation, our method utilizes a standard binary quantile regression model to handle the selection issue, thereby accommodating general heterogeneity in both the selection and outcome equations. We adopt a…

一般处理模型的高阶去偏估计量AI 解读

We have witnessed tremendous progress in developing the foundation for econometrics and causal inference in the past decades. The most popular paradigm is the classical (first-order) semiparametric theory, in which a key building block is the (first-order) influence function. However, it is well known that estimators based on influence functions can be sub-optimal in various settings. To address this issue, higher-order influence functions (HOIF) are developed. However, most…

自适应采集数据的随机化检验AI 解读

Randomization testing is a fundamental method in statistics, enabling inferential tasks such as testing for (conditional) independence of random variables, constructing confidence intervals in semiparametric location models, and constructing (by inverting a permutation test) model-free prediction intervals via conformal prediction. Randomization tests are exactly valid for any sample size, but their use is generally confined to exchangeable data. Yet in many applications,…

异质站点下单轮通信的分布式半参数推断AI 解读

Distributed inference has proven useful in contexts where data privacy is the central concern. However, privacy constraints often necessitate cumbersome coordination to exchange summary data during distributed learning. Most existing methods require two rounds of data communication, yet excessive communication rounds might discourage participation from local sites. This study develops distributed algorithms that require only a single communication round while maintaining…

一类半参数多项选择模型的闭式估计量AI 解读

We consider a semiparametric multinomial choice model that allows for an arbitrary joint distribution of choice specific unobservables that are independent of explanatory variables. This model permits relatively flexible substitution patterns between choices. To minimize computational difficulties, we restrict attention on estimators of the model that can be expressed in closed form. We combine and extend various results from the existing literature to enforce economic…

作为线性回归的增广平衡权重AI 解读

Abstract We provide a novel characterization of augmented balancing weights, also known as automatic debiased machine learning. These popular doubly robust estimators combine outcome modelling with balancing weights—weights that achieve covariate balance directly instead of estimating and inverting the propensity score. When the outcome and weighting models are both linear in some (possibly infinite) basis, we show that the augmented estimator is equivalent to a single…

通过函数逼近对随机趋势进行典型相关分析AI 解读

This paper proposes a novel approach for semiparametric inference on the number s of common trends and their loading matrix ψ in I(1)/I(0) systems. It combines functional approximation of limits of random walks and canonical correlation analysis, performed between the p observed time series of length T and the first K discretized elements of an L2 basis. Tests and selection criteria for s and estimators and tests on ψ are proposed; their properties are discussed as T and K…

右删失事件时间预测集的双重稳健高效校准AI 解读

Abstract Our objective is to construct well-calibrated prediction sets for a time-to-event outcome subject to right censoring with guaranteed coverage. Inspired by modern conformal inference, our approach avoids the need for a well-specified parametric or semiparametric survival model. Unlike existing conformal methods for survival data, which assume Type-I censoring with fully observed censoring times, we consider the more common right-censoring setting in which only the…

利用南极冰芯数据评估温度与冰积累速率的半参数模型AI 解读

In this paper, we present a semiparametric model for describing the effect of temperature on Antarctic ice accumulation on a paleoclimatic time scale. The model is motivated by sharp ups and downs in the rate of ice accumulation apparent from ice core data records, which are synchronous with movements of temperature. We prove consistency of the estimators under reasonable conditions. We conduct extensive simulations to assess the performance of the estimators and their…

Cox 模型的半参数 Fiducial 推断AI 解读

Abstract R.A. Fisher introduced the fiducial distribution as a potential replacement for the Bayesian posterior distribution in the 1930s. During the past century, fiducial approaches have been explored in various parametric and nonparametric settings. However, to the best of our knowledge, no fiducial inference has been developed in the realm of semiparametric statistics. In this paper, we propose a novel fiducial approach for semiparametric models. In memory of Sir David…

经济与金融中的贝叶斯方法：统一综述与分类AI 解读

This special issue brings together 21 papers that reflect the expanding scope and methodological evolution of Bayesian econometrics. We introduce a new organizing taxonomy that distinguishes between Traditional Bayesian Approaches - rooted in conjugate priors, standard likelihood-based estimation, and established Markov Chain Monte Carlo (MCMC) techniques - and Contemporary Bayesian Frontiers, characterized by nonparametric methods, variational inference, high-dimensional…

Copula 密度的半参数贝叶斯估计AI 解读

We propose a semiparametric Bayesian estimator for copula density, formulated as a generalized exponential family that combines a parametric baseline copula with a flexible adjustment modeled by a Gaussian Process (GP). The baseline captures the main features of dependence and accommodates possibly unbounded densities, while the GP component introduces nonparametric refinement. To modulate the influence of the baseline, we further introduce a tuning parameter that adjusts…

含持续与暂时无效率的半参数随机前沿模型贝叶斯估计AI 解读

We propose kernel estimators for the densities of persistent and transient inefficiencies in a generalized panel data stochastic frontier model. This approach relaxes the conventional distributional assumptions imposed on the inefficiencies, thereby increasing robustness. A Bayesian sampling algorithm is developed to estimate all model parameters and unobserved entities, as well as the bandwidths needed to construct the densities of persistent and transient inefficiencies.…

序数 Probit 模型的半参数贝叶斯估计：跨国、年龄与性别的生活满意度AI 解读

We employ a novel semiparametric Bayesian ordinal probit model to re-examine the relationships between age and life satisfaction (“happiness”) across countries and gender. Within our ordinal choice framework we introduce a new scheme for cutpoint simulation and develop a locally adaptive method for smoothing an otherwise erratic collection of age dummy variable coefficients. We find strong evidence that employment status is deeply intertwined with the reported happiness of…

混杂变量非可忽略缺失下中介分析的有效非参数推断AI 解读

Mediation analysis is widely used for exploring treatment mechanisms; however, it faces challenges when nonignorable missing confounders are present. Efficient inference of mediation effects and the efficiency loss due to nonignorable missingness have been rarely studied in the literature because of the difficulties arising from the ill-posed inverse problem. In this paper, we propose a general shadow variable framework for identifying mediation effects, allowing shadow…

利用双重机器学习估计直接与间接分位数处理效应AI 解读

We propose a framework to disentangle the quantile treatment effect of a binary treatment at a specific rank into an indirect quantile treatment effect that operates through a mediator and an unmediated direct quantile treatment effect. We establish identification results for these effects under the sequential ignorability assumption and propose double/debiased machine learning estimators, based on the efficient influence functions of the cumulative distribution functions of…

利用情感分析改进投资决策：概率排序框架AI 解读

We develop a probabilistic framework to extract sentiment information from text by training a model to predict and rank sentiments in newly encountered documents. Our approach imposes a joint semi-parametric model on text and ordinal response variables, addressing the challenges of sparse sentiment signals and complex response distributions. Through a word screening procedure and the use of normalized ranks, our approach achieves consistent sentiment ranking without…

迈向个体层面数据融合的统一半参数理论AI 解读

We consider inference about a finite-dimensional parameter integrating samples from independent sources. A recently developed theory considers scenarios where sources align with subsets of the conditional distributions of a single factorization of the joint target distribution. While this theory applies in many settings, it falls short in important data fusion problems, such as two-sample instrumental variable analysis, settings that integrate data from epidemiological…

利用最优传输构建多属性数据的Copula图模型AI 解读

Abstract Motivated by modern data types such as images and multi-view data, the multi-attribute graphical model aims to uncover conditional independence structures among vector-valued nodes. Under the Gaussian assumption, such independence is encoded in blockwise zeros of the precision matrix. To relax the restrictive Gaussian assumption, we propose a semiparametric multi-attribute graphical model leveraging a newly introduced cyclically monotone copula. This copula treats…

具有已知极点的密度及其导数：非参数估计的一致收敛率AI 解读

We study the uniform convergence rates of nonparametric estimators for a probability density function and its derivatives when the density has a known pole. Such situations arise in some structural microeconometric models, for example, in auction, labor, and consumer search, where uniform convergence rates of density functions are important for nonparametric and semiparametric estimation. Existing uniform convergence rates based on Rosenblatt’s kernel estimator are derived…

没有工具变量时的边际处理效应AI 解读

ABSTRACT We propose a method for defining, identifying, and estimating the marginal treatment effect (MTE) without imposing the instrumental variable (IV) assumptions of independence, exclusion, and separability (or monotonicity). Under a new definition of the MTE based on reduced‐form treatment error that is statistically independent of the covariates, we find that the relationship between the MTE and standard treatment parameters holds in the absence of IVs. We provide a…

股票指数间的跳跃传染：来自期权市场的证据AI 解读

We analyze the contagious propagation of jumps among international stock market indices, using a rich panel of high-frequency stock and options data ( 692,892 option contracts) over the period 2006–2015. We propose a bivariate option pricing model designed to allow for time and space amplification of jumps in option markets. We develop a semi-parametric estimation procedure, which employs a continuum of moment conditions in GMM with implied states and non-parametric…

结果延迟下的协变量调整响应自适应设计AI 解读

Covariate-adjusted response-adaptive (CARA) designs have gained widespread adoption for their clear benefits in enhancing experimental efficiency and participant welfare. These designs dynamically adjust treatment allocations during interim analyses based on participant responses and covariates collected during the experiment. However, delayed responses can significantly compromise the effectiveness of CARA designs, as they hinder timely adjustments to treatment assignments…