论文检索

空间自回归模型的高阶最小二乘推断AI 解读

We develop refined inference for spatial regression models with predetermined regressors. The ordinary least squares estimate of the spatial parameter is neither consistent nor asymptotically normal, unless the elements of the spatial weight matrix uniformly vanish as sample size diverges. We develop refined testing of the hypothesis of no spatial dependence, without requiring such negligibility of spatial weights, by formal Edgeworth expansions. We also develop such…

空间样本选择模型估计：部分极大似然方法AI 解读

We study estimation of sample selection models with the spatially lagged latent dependent variable or spatial errors in both the selection and outcome equations under cross-sectional dependence. Since there is no estimation framework for the spatial-lag model and the existing estimators for the spatial-error model are computationally demanding or have poor small sample properties, we suggest to estimate these models by the partial maximum likelihood estimator. We show that…

含不可观测异质性的空间面板分位数模型AI 解读

This paper introduces a spatial panel quantile model with unobserved heterogeneity. The proposed model is capable of capturing high-dimensional cross-sectional dependence and allows heterogeneous regression coefficients. For estimating model parameters, a new estimation procedure is proposed. When both the time and cross-sectional dimensions of the panel go to infinity, the uniform consistency and the asymptotic normality of the estimated parameters are established. In order…

谁应接种疫苗？SIR 网络上的个性化疫苗分配AI 解读

How to allocate vaccines over heterogeneous individuals is one of the important policy decisions in pandemic times. This paper develops a procedure to estimate an individualized vaccine allocation policy under limited supply, exploiting social network data containing individual demographic characteristics and health status. We model the spillover effects of vaccination based on a Heterogeneous-Interacted-SIR network model and estimate an individualized vaccine allocation…

地理位置不匹配数据的空间计量方法AI 解读

We produce methodology for regression analysis when the geographic locations of the independent and dependent variables do not coincide, in which case we speak of misaligned data. We develop and investigate two complementary methods for regression analysis with misaligned data that circumvent the need to estimate or specify the covariance of the regression errors. We carry out a detailed reanalysis of Maccini and Yang (2009) and find economically significant quantitative…

大规模空间自回归模型的高效闭式估计AI 解读

Newton-step approximations to pseudo maximum likelihood estimates of spatial autoregressive models with a large number of parameters are examined, in the sense that the parameter space grows slowly as a function of sample size. These have the same asymptotic efficiency properties as maximum likelihood under Gaussianity but are of closed form. Hence they are computationally simple and free from compactness assumptions, thereby avoiding two notorious pitfalls of implicitly…

通过剖面化与袋装法估计稀疏时空自回归模型AI 解读

We consider a new class of spatio-temporal models with sparse autoregressive coefficient matrices and exogenous variable. To estimate the model, we first profile the exogenous variable out of the response. This leads to a profiled model structure. Next, to overcome endogeneity issue, we propose a class of generalized methods of moment (GMM) estimators to estimate the autoregressive coefficient matrices. A novel bagging-based estimator is further developed to conquer the…

结构VAR与金融网络：空间建模的最小距离方法AI 解读

Summary In this paper, I interpret a time series spatial model (T‐SAR) as a constrained structural vector autoregressive (SVAR) model. Based on these restrictions, I propose a minimum distance approach to estimate the (row‐standardized) network matrix and the overall network influence parameter of the T‐SAR from the SVAR estimates. I also develop a Wald‐type test to assess the distance between these two models. To implement the methodology, I discuss machine learning methods…

外生冲击的非随机暴露AI 解读

We develop a new approach to estimating the causal effects of treatments or instruments that combine multiple sources of variation according to a known formula. Examples include treatments capturing spillovers in social or transportation networks and simulated instruments for policy eligibility. We show how exogenous shocks to some—but not all—determinants of such variables can be leveraged while avoiding omitted variables bias. Our solution involves specifying…

短期主义的宏观影响AI 解读

R&D investment reduces current profits, so short‐term pressure to hit profit targets may distort R&D. In the data, firms just meeting Wall Street forecasts have lower R&D growth and subsequent innovation, while managers just missing receive lower pay. But short‐termist distortions might not quantitatively matter if aggregation or equilibrium dampen their impact. So I build and estimate a quantitative endogenous growth model in which short‐termism arises naturally as…

网络聚类稳健推断AI 解读

Since network data commonly consists of observations from a single large network, researchers often partition the network into clusters in order to apply cluster‐robust inference methods. Existing such methods require clusters to be asymptotically independent. Under mild conditions, we prove that, for this requirement to hold for network‐dependent data, it is necessary and sufficient that clusters have low conductance, the ratio of edge boundary size to volume. This yields a…

图极限博弈：网络博弈与干预的统计框架AI 解读

In this paper, we present a unifying framework for analyzing equilibria and designing interventions for large network games sampled from a stochastic network formation process represented by a graphon. To this end, we introduce a new class of infinite population games, termed graphon games , in which a continuum of heterogeneous agents interact according to a graphon, and we show that equilibria of graphon games can be used to approximate equilibria of large network games…

高维关联度表估计：从部门溢出理解大缓和AI 解读

We estimate sectoral spillovers around the Great Moderation with the help of forecast error variance decomposition tables. Obtaining such tables in high dimensions is challenging because they are functions of the estimated vector autoregressive coefficients and the residual covariance matrix. In a simulation study, we compare various regularization methods on both and conduct a comprehensive analysis of their performance. We show that standard estimators of large…

横截面依赖下的自助法推断AI 解读

In this paper, we introduce a method of generating bootstrap samples with unknown patterns of cross‐ sectional/spatial dependence, which we call the spatial dependent wild bootstrap. This method is a spatial counterpart to the wild dependent bootstrap of Shao (2010) and generates data by multiplying a vector of independently and identically distributed external variables by the eigendecomposition of a bootstrap kernel. We prove the validity of our method for studentized and…

高斯过程协方差参数的贝叶斯固定域渐近理论AI 解读

Gaussian process models typically contain finite-dimensional parameters in the covariance function that need to be estimated from the data. We study the Bayesian fixed-domain asymptotics for the covariance parameters in a universal kriging model with an isotropic Matérn covariance function, which has many applications in spatial statistics. We show that when the dimension of domain is less than or equal to three, the joint posterior distribution of the microergodic parameter…

大型网络扩散的分解分析AI 解读

Diffusion over a network refers to the phenomenon of a change of state of a cross-sectional unit in one period leading to a change of state of its neighbors in the network in the next period. One may estimate or test for diffusion by estimating a cross-sectionally aggregated correlation between neighbors over time from data. However, the estimated diffusion can be misleading if the diffusion is confounded by omitted covariates. This paper focuses on the measure of diffusion…

基于似然的稀疏协方差估计：近端距离算法AI 解读

Summary This paper addresses the task of estimating a covariance matrix under a patternless sparsity assumption. In contrast to existing approaches based on thresholding or shrinkage penalties, we propose a likelihood-based method that regularizes the distance from the covariance estimate to a symmetric sparsity set. This formulation avoids unwanted shrinkage induced by more common norm penalties, and enables optimization of the resulting nonconvex objective by solving a…

部分干扰下网络处理效应的有效半参数估计AI 解读

Summary Although many estimators for network treatment effects have been proposed, their optimality properties, in terms of semiparametric efficiency, have yet to be resolved. We present a simple yet flexible asymptotic framework for deriving the efficient influence function and the semiparametric efficiency lower bound for a family of network causal effects under partial interference. An important corollary of our results is that one existing estimator, that proposed by Liu…

干扰下的平均直接与间接因果效应AI 解读

Summary We propose a definition for the average indirect effect of a binary treatment in the potential outcomes model for causal inference under cross-unit interference. Our definition is analogous to the standard definition of the average direct effect and can be expressed without needing to compare outcomes across multiple randomized experiments. We show that the proposed indirect effect satisfies a decomposition theorem stating that in a Bernoulli trial, the sum of the…

高度多元空间数据的图结构高斯过程模型AI 解读

Summary For multivariate spatial Gaussian process models, customary specifications of cross-covariance functions do not exploit relational inter-variable graphs to ensure process-level conditional independence between the variables. This is undesirable, especially in highly multivariate settings, where popular cross-covariance functions, such as multivariate Matérn functions, suffer from a curse of dimensionality as the numbers of parameters and floating-point operations…

时空数据的因果推断：估计伊拉克空袭对叛乱暴力的影响AI 解读

Abstract Many causal processes have spatial and temporal dimensions. Yet the classic causal inference framework is not directly applicable when the treatment and outcome variables are generated by spatio-temporal point processes. We extend the potential outcomes framework to these settings by formulating the treatment point process as a stochastic intervention. Our causal estimands include the expected number of outcome events in a specified area under a particular…

含噪网络关联数据的线性回归与推断AI 解读

Abstract Linear regression on network-linked observations has been an essential tool in modelling the relationship between response and covariates with additional network structures. Previous methods either lack inference tools or rely on restrictive assumptions on social effects and usually assume that networks are observed without errors. This paper proposes a regression model with non-parametric network effects. The model does not assume that the relational data or…

CovNet：多维定义域函数型数据的协方差网络AI 解读

Abstract Covariance estimation is ubiquitous in functional data analysis. Yet, the case of functional observations over multidimensional domains introduces computational and statistical challenges, rendering the standard methods effectively inapplicable. To address this problem, we introduce Covariance Networks (CovNet) as a modelling and estimation tool. The CovNet model is universal—it can be used to approximate any covariance up to desired precision. Moreover, the model…

含高阶网络相互依赖的多元面板Probit贝叶斯估计：广东企业全球市场参与AI 解读

Summary This paper proposes a Bayesian estimation framework for panel data sets with binary dependent variables where a large number of cross‐sectional units are observed over a short period of time and cross‐sectional units are interdependent in more than a single network domain. The latter provides for a substantial degree of flexibility towards modeling the decay function in network neighborliness (e.g., by disentangling the importance of rings of neighbors) or towards…