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精度矩阵最小二乘：高维估计与推断AI 解读

The least squares estimator can be cast as depending only on the precision matrix of the data, similar to the weights of a global minimum variance portfolio. We give conditions under which any plug-in precision matrix estimator produces an unbiased and consistent least squares estimator for stationary time series regressions, in both low- and high-dimensional settings. Such conditions define a class of “Precision Least Squares” (PrLS) estimators, which are shown to be…

函数值空间过程的谱密度估计AI 解读

The spectral density function describes the second-order properties of a stationary stochastic process on Rd. This paper considers the nonparametric estimation of the spectral density of a continuous-time stochastic process taking values in a separable Hilbert space. Our estimator is based on kernel smoothing and can be applied to a wide variety of spatial sampling schemes including those in which data are observed at irregular spatial locations. Thus, it finds immediate…

函数依赖在空间计量经济学中的应用AI 解读

In this paper, we generalize the concept of functional dependence (FD) from time series (see Wu [2005, Proceedings of the National Academy of Sciences 102, 14150–14154]) and stationary random fields (see El Machkouri, Volný, and Wu [2013, Stochastic Processes and Their Applications 123, 1–14]) to nonstationary spatial processes. Within conventional settings in spatial econometrics, we define the concept of spatial FD measure and establish a moment inequality, an exponential…

空间自回归模型中的异方差稳健设定检验AI 解读

Spatial autoregressive (SAR) and related models offer flexible yet parsimonious ways to model spatial and network interactions. SAR specifications typically rely on a particular parametric functional form and an exogenous choice of the so-called spatial weight matrix with only limited guidance from theory in making these specifications. Also, the choice of a SAR model over other alternatives, such as spatial Durbin (SD) or spatial lagged X (SLX) models, is often arbitrary,…

空间相关数据模型的Moran’s I lasso估计AI 解读

Summary This paper proposes a lasso-based estimator which uses information embedded in the Moran statistic to develop a selection procedure called Moran’s I lasso (Mi-lasso) to solve the eigenvector spatial filtering (ESF) eigenvector selection problem. ESF uses a subset of eigenvectors from a spatial weights matrix to efficiently account for any omitted spatially correlated terms in a classical linear regression framework, thus eliminating the need for the researcher to…

多重结构突变与多因子误差下空间面板模型的收缩估计AI 解读

This study investigates spatial panel data models with a multifactor error structure and multiple structural breaks occurring in the coefficients of both spatial lagged and explanatory variables. While extensive research has addressed cross-sectional dependence in panel data, including approaches that integrate spatial and factor structures within a single framework, few studies account for time-varying model parameters and achieving consistent estimation remains a…

含结构变化的空间面板数据模型AI 解读

Spatial panel data models have gained widespread application in social sciences, particularly in economics, due to their ability to capture spatial dependencies. However, existing studies on spatial models typically rely on the assumption of parameter stability, which may be overly restrictive given the well-documented prevalence of structural changes in economic relationships. This paper addresses this limitation by proposing and analyzing spatial panel data models that…

具有近最优收敛率的动态空间自回归稳健估计AI 解读

Spatial autoregression has been extensively studied in various applications, yet its robust estimation methods have received limited attention. In this work, we introduce two dynamic spatial autoregression (DSAR) models aimed at capturing temporal trends and depicting the asymmetric network effects of the units. For both DSAR models, we propose a truncated Yule–Walker estimation method, which is tailored to achieve robust estimation in the presence of heavy-tailed data.…

内生网络中二元结果的同伴效应：战略互补与从众偏好AI 解读

ABSTRACT This paper introduces a generalized model of peer effects for binary outcomes, based on a network game that accounts for strategic complementarity (influence of the number of peers that select the same action) and conformity to social norms (penalizing deviations from the average peers' action). We derive conditions for a unique Bayesian Nash equilibrium under rational expectations about peers' behavior. Unobserved individual heterogeneity affecting network…

低秩随机矩阵特征向量的逐元素高阶分析：偏差校正、Edgeworth 展开与自助法AI 解读

Understanding the distributions of spectral estimators in low-rank random matrix models, also known as signal-plus-noise matrix models, is fundamentally important in various statistical learning problems, including network analysis, matrix denoising and matrix completion. This paper investigates the entrywise eigenvector distributions in a broad range of low-rank signal-plus-noise matrix models by establishing their higher-order accurate stochastic expansions. At a high…

纵向网络数据的半参数建模与分析AI 解读

We introduce a semiparametric latent space model for analyzing longitudinal network data. The model consists of a static latent space component and a time-varying node-specific baseline component. We develop a semiparametric efficient score equation for the latent space parameter by adjusting for the baseline nuisance component. Estimation is accomplished through a one-step update estimator and an appropriately penalized maximum likelihood estimator. We derive oracle error…

工作场所同伴对未来工资的影响AI 解读

ABSTRACT This paper examines workplace peer effects in two directions, leveraging employer‐employee data for Italy. First, using a novel estimation approach and addressing endogenous worker‐peer sorting, we estimate that a 10% increase in peer quality raises one's wage by 1.8% in the next year. The effect declines to 0.7% after 5 years. Second, in an event study around mobility episodes, we quantify wage changes associated with the entry and leave of high‐quality and…

广义线性网络特征模型中用于维度选择的尖峰与厚尾先验AI 解读

Summary Latent space models are often used to model network data by embedding a network’s nodes into a low-dimensional latent space; however, choosing the dimension of this space remains a challenge. To this end, we begin by formalizing a class of latent space models we call generalized linear network eigenmodels that can model various edge types (binary, ordinal, nonnegative continuous) found in scientific applications. This model class subsumes the traditional eigenmodel…

面向大规模时空协变量的空间效应检测回归AI 解读

Abstract We develop a Spatial Effect Detection Regression (SEDR) model to capture the nonlinear and irregular effects of high-dimensional spatio-temporal predictors on a scalar outcome. Specifically, we assume that both the component and the coefficient functions in the SEDR are unknown smooth functions of location and time. This allows us to leverage spatially and temporally correlated information, transforming the curse of dimensionality into a blessing, as confirmed by…

没有免费午餐？经由专家中介销售企业的福利分析AI 解读

Abstract We study how firms target and influence expert intermediaries. In our context, pharmaceutical manufacturers provide payments to physicians during promotional interactions. We develop an identification strategy based on plausibly exogenous variation in payments driven by differential exposure to spillovers from AMC CoI policies. Using a case study of an important class of cardiovascular drugs, we estimate heterogeneous effects of payments on prescribing, with firms…

从面板数据识别网络联系：理论及税收竞争应用AI 解读

Abstract Social interactions determine many economic behaviours, but information on social ties does not exist in most publicly available and widely used datasets. We present results on the identification of social networks from observational panel data that contains no information on social ties between agents. In the context of a canonical social interactions model, we provide sufficient conditions under which the social interactions matrix, endogenous and exogenous social…

兼顾多样性与异质受欢迎程度的超图建模AI 解读

While relations among individuals make an important part of data with scientific and business interests, existing statistical modeling of relational data has mainly been focusing on dyadic relations, that is, those between two individuals.This article addresses the less studied, though commonly encountered, polyadic relations that can involve more than two individuals.In particular, we propose a new latent space model for hypergraphs using determinantal point processes,…

多层度修正随机块模型中的联合谱聚类AI 解读

Modern network datasets are often composed of multiple layers, resulting in collections of networks over the same set of vertices but with potentially different connectivity patterns on each network. These data require models and methods that are flexible enough to capture local and global differences across the networks while at the same time being parsimonious and tractable to yield computationally efficient and theoretically sound solutions that are capable of aggregating…

网络辅助回归的保形预测AI 解读

An important problem in network analysis is predicting a node attribute using both network covariates, such as graph embedding coordinates or local subgraph counts, and conventional node covariates, such as demographic characteristics. While standard regression methods that make use of both types of covariates may be used for prediction, statistical inference is complicated by the fact that the nodal summary statistics are often dependent in complex ways. We show that under…

深度 Fréchet 回归AI 解读

Advancements in modern science have led to the increasing availability of non-Euclidean data in metric spaces. This paper addresses the challenge of modeling relationships between non-Euclidean responses and multivariate Euclidean predictors. We propose a flexible regression model capable of handling high-dimensional predictors without imposing parametric assumptions. Two primary challenges are addressed: the curse of dimensionality in nonparametric regression and the…

块模型家族的网络拟合优度检验AI 解读

The block-model family has four popular network models (SBM, DCBM, MMSBM, and DCMM). A fundamental problem is, how well each of these models fits with real networks. We propose GoF-MSCORE as a new Goodness-of-Fit (GoF) metric for DCMM (the broadest one among the four), with two main ideas. The first is to use cycle count statistics as a general recipe for GoF. The second is a novel network fitting scheme. GoF-MSCORE is a flexible GoF approach, and we further extend it to…

复杂区域上分位数空间变系数模型的估计与推断AI 解读

This article presents a flexible quantile spatially varying coefficient model (QSVCM) for the regression analysis of spatial data. The proposed model enables researchers to assess the dependence of conditional quantiles of the response variable on covariates while accounting for spatial nonstationarity. Our approach facilitates learning and interpreting heterogeneity in spatial data distributed over complex or irregular domains. We introduce a quantile regression method that…

通过自适应合并实现纵向网络的高效估计AI 解读

Longitudinal networks consist of sequences of temporal edges among multiple nodes, where the temporal edges are observed in real-time. They have become ubiquitous with the rise of online social platforms and e-commerce, but largely under-investigated in the literature. In this article, we propose an efficient estimation framework for longitudinal networks, leveraging strengths of adaptive network merging, tensor decomposition, and point processes. It merges neighboring…

零膨胀与离群计数的稳健贝叶斯建模：理论稳健性与高效计算AI 解读

Count data with zero inflation and large outliers are ubiquitous in many scientific applications. However, posterior analysis under a standard statistical model, such as Poisson or negative binomial distribution, is sensitive to such contamination. This study introduces a novel framework for Bayesian modeling of counts that is robust to both zero inflation and large outliers. In doing so, we introduce rescaled beta distribution and adopt it to absorb undesirable effects from…