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具有高阶精度的双样本网络推断与网络哈希AI 解读

Two-sample hypothesis testing for network comparison presents many significant challenges, including: leveraging repeated network observations and known node registration, but without requiring them to operate; relaxing strong structural assumptions; achieving finite-sample higher-order accuracy; handling different network sizes and sparsity levels; fast computation and memory parsimony; controlling false discovery rate (FDR) in multiple testing; and theoretical…

高维空间面板网络的统一推断AI 解读

We propose employing a high-dimensional generalized method of moments (GMM) estimator, regularized for dimension reduction and subsequently debiased to correct for shrinkage bias (referred to as a debiased-regularized estimator), for inference on large-scale spatial panel networks. In particular, the network structure, which incorporates a flexible sparse deviation that can be regarded either as a latent component or as a misspecification of a predetermined adjacency matrix,…

大型 VAR 中溢出发送组与接收组的识别AI 解读

I propose an algorithm that partitions the series of a large vector autoregression (VAR) into groups based on the spillover structure. The novelty of the procedure is that it is capable of simultaneously detecting both the giver and receiver group structures. I study the properties of the algorithm when the data are generated by a class of network-based VAR models and show that it consistently detects the groups within this class. The methodology is applied to study the…

对象值时间序列的变点检测AI 解读

This article is concerned with change point detection for object-valued data that reside in a metric space, which has attracted some recent interests in statistics and econometrics literature. The existing methods either focus on independent data or can only detect change in the Fréchet mean or variance. In this article, we propose a self-normalization (SN, hereafter) based statistic for detecting a shift in the marginal distribution of object-valued time series. Our test is…

有向网络半参数形成模型的推断AI 解读

We propose a semiparametric model for dyadic link formations in directed networks. The model contains a set of degree parameters that measure different effects of popularity or outgoingness across nodes, a regression parameter vector that reflects the homophily effect resulting from the nodal attributes or pairwise covariates associated with edges, and a set of latent random noises with unknown distributions. Our interest lies in inferring the unknown degree parameters and…

动态分位数的函数系数 VAR 模型及非参数金融网络构建AI 解读

In this article, we investigate a functional coefficient vector autoregressive model for conditional quantiles, in which the interdependences among tail risks such as Value-at-Risk are allowed to vary smoothly with a variable of general economy. Methodologically, we develop an easy-to-implement two-stage procedure to estimate functionals in the dynamic network system based on the deep learning method of neural networks and the local linear smoothing technique. We establish…

二元关系数据的双重／去偏机器学习AI 解读

This article presents novel methods and theories for estimation and inference about parameters in statistical models using machine learning for nuisance parameter estimation when data are dyadic. We propose a dyadic cross-fitting method to remove over-fitting biases under arbitrary dyadic dependence. Together with the use of Neyman orthogonal scores, this novel cross-fitting method enables root- n consistent estimation and inference robustly against dyadic dependence. We…

具有网络关联固定效应的动态面板分位数回归AI 解读

This paper introduces a dynamic panel data quantile regression model with network-linked fixed effects, named DQR-NFE, in which unobserved individual heterogeneity is structured through an underlying network. The corresponding estimator is derived by incorporating a quantile network cohesion (QNC) penalty into the dynamic panel quantile regression framework. This penalty encourages connected units within the network to exhibit similar conditional quantiles, with a…

协变量自适应随机化下两阶段实验的推断AI 解读

This paper studies inference in two-stage randomized experiments under covariate-adaptive randomization. In the initial stage of this experimental design, clusters (e.g., households, schools, or graph partitions) are stratified and randomly assigned to control or treatment groups based on cluster-level covariates. Subsequently, an independent second-stage design is carried out, wherein units within each treated cluster are further stratified and randomly assigned to either…

大量参与者下的策略性网络形成AI 解读

We derive asymptotic approximations for models of strategic network formation, where limits are taken as the number of nodes (agents) increases to infinity. Our framework assumes a random utility model where agents have heterogeneous tastes over links, and payoffs allow for anonymous and non-anonymous interaction effects, and the observed network is assumed to be pairwise stable. Our main results concern convergence of the link intensity from finite pairwise stable networks…

含固定效应的非线性网络数据模型的刀切偏差校正AI 解读

I introduce a new method for bias correction of dyadic models with agent-specific fixed effects, including the dyadic link formation model with homophily and degree heterogeneity. The proposed approach uses a jackknife procedure to deal with the incidental parameters problem. The method can be applied to both directed and undirected networks, allows for non-binary outcome variables, and can be used to bias correct estimates of average effects and counterfactual outcomes. I…

无需指定网络结构的同伴效应检验AI 解读

This paper proposes an Anderson–Rubin (AR) test for the presence of peer effects in panel data without the need to specify the network structure. The unrestricted model of our test is a linear panel data model of social interactions with dyad-specific peer effect coefficients for all potential peers. The proposed AR test evaluates if these peer effect coefficients are all zero. As the number of peer effect coefficients increases with the sample size, so does the number of…

空间处理的因果推断AI 解读

Many events and policies (treatments) occur at specific spatial locations, with researchers interested in their effects on nearby units. I approach the spatial treatment setting from an experimental perspective: What ideal experiment would we design to estimate the causal effects of spatial treatments? This perspective motivates a comparison between units near realized treatment locations and units near counterfactual (unrealized) candidate locations, which differs from…

细粒度情境中的空间经济学AI 解读

We examine the application of quantitative spatial models to the growing body of fine spatial data used to study local economic outcomes. In granular settings in which people choose from a large set of potential residence‐workplace pairs, observed outcomes in part reflect idiosyncratic choices. Using analytical examples, Monte Carlo simulations, and event studies of neighborhood employment booms, we demonstrate that calibration procedures that equate observed shares and…

网络干扰下因果推断的局部方法AI 解读

We propose a new nonparametric modeling framework for causal inference when outcomes depend on how agents are linked in a social or economic network. Such network interference describes a large literature on treatment spillovers, social interactions, social learning, information diffusion, disease and financial contagion, social capital formation, and more. Our approach works by first characterizing how an agent is linked in the network using the configuration of other…

动态网络的双重异质性模型AI 解读

Analysis of networks that evolve dynamically requires the joint modelling of individual snapshots and time dynamics. This paper proposes a new flexible two-way heterogeneity model towards this goal. The new model equips each node of the network with two heterogeneity parameters, one to characterize the propensity to form ties with other nodes statically and the other to differentiate the tendency to retain existing ties over time. With n observed networks each having p…

网络干扰下基于平均场方法的因果效应估计AI 解读

We study causal effect estimation from observational data under interference. The interference pattern is captured by an observed network. We adopt the chain graph framework of (J. Amer. Statist. Assoc. 116 (2021) 833–844), which allows (i) interaction among the outcomes of distinct study units connected along the graph and (ii) long range interference, whereby the outcome of an unit may depend on the treatments assigned to distant units connected along the interference…

Abstract This article presents and analyzes an approach to cluster-based inference for dependent data. The primary setting considered here is with spatially indexed data in which the dependence structure of observed random variables is characterized by a known, observed dissimilarity measure over spatial indices. Observations are partitioned into clusters with the use of an unsupervised clustering algorithm applied to the dissimilarity measure. Once the partition into…

数值型成对比较数据的最小二乘估计AI 解读

Abstract Least square estimators for graphical models for cardinal paired comparison data with and without covariates are rigorously analysed. Novel, graph-based, necessary, and sufficient conditions that guarantee strong consistency, asymptotic normality, and the exponential convergence of the estimated ranks are emphasized. A complete theory for models with covariates is laid out. In particular, conditions under which covariates can be safely omitted from the model are…

SymmPI：具有群对称性数据的预测推断AI 解读

Abstract Quantifying the uncertainty of predictions is a core problem in modern statistics. Methods for predictive inference have been developed under a variety of assumptions, often—for instance, in standard conformal prediction—relying on the invariance of the distribution of the data under special groups of transformations such as permutation groups. Moreover, many existing methods for predictive inference aim to predict unobserved outcomes in sequences of feature-outcome…

网络实验中的因果推断：回归分析与基于设计的性质AI 解读

Network experiments are powerful tools for studying spillover effects, which avoid endogeneity by randomly assigning treatments to units over networks. However, it is non-trivial to analyze network experiments properly without imposing strong modeling assumptions. We show that regression-based point estimators and standard errors can have strong theoretical guarantees if the regression functions and robust standard errors are carefully specified to accommodate the…

控制错误发现率的稳健共同基金筛选AI 解读

In this article, we address the challenge of identifying well-performing mutual funds among a large pool of candidates, utilizing the linear factor pricing model. Assuming observable factors with a weak correlation structure for the idiosyncratic error, we propose a spatial-sign based multiple testing procedure (SS-BH). When latent factors are present, we first extract them using the elliptical principle component method (He et al. 2022) and then propose a factor-adjusted…

利用 Copula 处理空间自回归模型中的内生性AI 解读

This paper develops a novel, instrument-free semiparametric copula framework for a spatial autoregressive (SAR) model to address endogeneity arising from an endogenous spatial weights matrix, endogenous regressors, or both. Moving beyond conventional Gaussian copulas, we develop a flexible estimator based on the Student’s t copula with an unknown degrees-of-freedom (df) parameter, which nests the Gaussian case and allows the data to determine the presence of tail dependence.…

含非参数内生效应的空间自回归面板模型估计AI 解读

This paper proposes a sieve generalized method of moments (GMM) method for the estimation of spatial autoregressive panel data models with nonparametric endogenous effect. The new estimator incorporates both linear moments based on the orthogonality of the exogenous regressors with the model disturbances and quadratic moments based on the properties of idiosyncratic errors. We establish the consistency and asymptotic normality of the sieve GMM estimator and show that it is…