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动态交互事件网络中的全局协变量效应检验AI 解读

In statistical network analysis it is common to observe so called interaction data. Such data is characterized by actors forming the vertices and interacting along edges of the network, where edges are randomly formed and dissolved over the observation horizon. In addition, covariates are observed and the goal is to model the impact of the covariates on the interactions. We distinguish two types of covariates: global, system-wide covariates (i.e., covariates taking the same…

基于条件分位数变分自编码器的资产定价AI 解读

We propose a new asset pricing model that is applicable to the big panel of return data. The main idea of this model is to learn the conditional distribution of the return, which is approximated by a step distribution function constructed from conditional quantiles of the return. To study conditional quantiles of the return, we propose a new conditional quantile variational autoencoder (CQVAE) network. The CQVAE network specifies a factor structure for conditional quantiles…

空间依赖面板数据的一致非参数推断AI 解读

This article proposes a uniform functional inference method for nonparametric regressions in a panel-data setting that features general unknown forms of spatio-temporal dependence. The method requires a long time span, but does not impose any restriction on the size of the cross section or the strength of spatial correlation. The uniform inference is justified via a new growing-dimensional Gaussian coupling theory for spatio-temporally dependent panels. We apply the method…

存在溢出效应的连续处理模型估计：控制函数方法AI 解读

We study a continuous treatment effect model in the presence of treatment spillovers through social networks.We assume that one's outcome is affected not only by his/her own treatment but also by a (weighted) average of his/her neighbors' treatments, both of which are treated as endogenous variables.Using a control function approach with appropriate instrumental variables, we show that the conditional mean potential outcome can be nonparametrically identified.We also…

固定效应矩阵指数非平衡面板模型估计：美国对外直接投资存量应用AI 解读

In this article, we consider a matrix exponential unbalanced panel data model that allows for (i) spillover effects using matrix exponential terms, (ii) unobserved heterogeneity across entities and time, and (iii) potential heteroscedasticity in the error terms across entities and time. We adopt a likelihood based direct estimation approach in which we jointly estimate the common parameters and fixed effects. To ensure that our estimator has the standard large sample…

加密货币的时变网络AI 解读

Cryptocurrencies return cross-predictability and technological similarity yield information on risk propagation and market segmentation. To investigate these effects, we build a time-varying network for cryptocurrencies, based on the evolution of return cross-predictability and technological similarities. We develop a dynamic covariate-assisted spectral clustering method to consistently estimate the latent community structure of cryptocurrencies network that accounts for…

非平稳系统的大规模溢出网络AI 解读

This article proposes a vector error correction framework for constructing large consistent spillover networks of nonstationary systems grounded in the network theory of Diebold and Y ilmaz. We aim to provide a tailored methodology for the large nonstationary (macro)economic and financial system application settings avoiding technical and often hard to verify assumptions for general statistical high-dimensional approaches where the dimension can also increase with sample…

动态网络分位数回归模型AI 解读

We propose a dynamic network quantile regression model to investigate the quantile connectedness using a predetermined network information. We extend the existing network quantile autoregression model of Zhu et al. by explicitly allowing the contemporaneous network effects and controlling for the common factors across quantiles. To cope with the endogeneity issue due to simultaneous network spillovers, we adopt the instrumental variable quantile regression (IVQR) estimation…

社交媒体网络变化的在线监测AI 解读

Econometricians are increasingly working with high-dimensional networks and their dynamics. Econometricians, however, are often confronted with unforeseen changes in network dynamics. In this article, we develop a method and the corresponding algorithm for monitoring changes in dynamic networks. We characterize two types of changes, edge-initiated and node-initiated, to feature the complexity of networks. The proposed approach accounts for three potential challenges in the…

通过随机扰动与矩估计实现 β 模型的边差分隐私估计AI 解读

A standing challenge in data privacy is the trade-off between the level of privacy and the efficiency of statistical inference. Here, we conduct an in-depth study of this trade-off for parameter estimation in the β-model (Ann. Appl. Probab. 21 (2011) 1400–1435) for edge differentially private network data released via jittering (J. R. Stat. Soc. Ser. C. Appl. Stat. 66 (2017) 481–500). Unlike most previous approaches based on maximum likelihood estimation for this network…

通过双向MOSUM对高维时间序列变点进行ℓ2推断AI 解读

We propose an inference method for detecting multiple change points in high-dimensional time series, targeting dense or spatially clustered signals. Our method aggregates moving sum (MOSUM) statistics cross-sectionally by an ℓ2-norm and maximizes them over time. We further introduce a novel Two-Way MOSUM, which utilizes spatial-temporal moving regions to search for breaks, with the added advantage of enhancing testing power when breaks occur in only a few groups. The…

空间依赖下的一致模型设定检验AI 解读

We propose a series-based nonparametric specification test for a regression function when data are spatially dependent, the “space” being of a general economic or social nature. Dependence can be parametric, parametric with increasing dimension, semiparametric or any combination thereof, thus covering a vast variety of settings. These include spatial error models of varying types and levels of complexity. Under a new smooth spatial dependence condition, our test statistic is…

学校多样性：移民与美国本土出生学生的教育表现AI 解读

Abstract We study the effect of exposure to immigrants on the educational outcomes of U.S.-born students, using a unique dataset combining population-level birth and school records from Florida. This research question is complicated by the substantial school selection of U.S.-born students, especially among White and comparatively affluent students, in response to the presence of immigrant students in the school. We propose a new identification strategy, comparing sibling…

社会互动、机制与均衡：学习时间和学业表现模型的证据AI 解读

We develop and estimate a model of student study time choices on a social network. The model is designed to exploit unique data in the Berea Panel Study. Study time data allow us to quantify an intuitive mechanism for academic social interactions: own study time may depend on friend study time in a heterogeneous manner. Social network data allow us to embed study time and resulting academic achievement in an estimable equilibrium framework. We develop a specification test…

无向二元关系数据的核密度估计AI 解读

We study nonparametric estimation of density functions for undirected dyadic random variables (i.e., random variables defined for all n≡defN2 unordered pairs of agents/nodes in a weighted network of order N). These random variables satisfy a local dependence property: any random variables in the network that share one or two indices may be dependent, while those sharing no indices in common are independent. In this setting, we show that density functions may be estimated by…

利用 Wasserstein 生成对抗网络设计蒙特卡洛模拟AI 解读

When researchers develop new econometric methods it is common practice to compare the performance of the new methods to those of existing methods in Monte Carlo studies. The credibility of such Monte Carlo studies is often limited because of the discretion the researcher has in choosing the Monte Carlo designs reported. To improve the credibility we propose using a class of generative models that has recently been developed in the machine learning literature, termed…

通过分布回归估计网络与面板的分位数效应AI 解读

This paper provides a method to construct simultaneous confidence bands for quantile functions and quantile effects in nonlinear network and panel models with unobserved two-way effects, strictly exogenous covariates, and possibly discrete outcome variables. The method is based upon projection of simultaneous confidence bands for distribution functions constructed from fixed effects distribution regression estimators. These fixed effects estimators are debiased to deal with…

非代表性网络样本：通过加权估计网络结构性质AI 解读

This paper analyzes statistical issues arising from non-representative samples of a network. Sampled network data could systematically bias the network properties and generate non-classical measurement error problems. Apart from the sampling rate and the elicitation procedure, the biases on network structural measures depend non-trivially on which subpopulations of nodes are missing with higher probability. We propose a methodology, adapting weighted estimators to networked…

对《暴露映射误设下的因果推断：区分定义与假设》的讨论：政策评价局限AI 解读

We thank Sävje (2024) for a thought-provoking article and appreciate the opportunity to share our perspective as social scientists. In his article, Sävje recommended misspecified exposure effects as a way to avoid strong assumptions about interference when analysing the results of an experiment. In this discussion, we highlight a key limitation of Sävje’s recommendation. Exposure effects are not generally useful for evaluating social policies without the strong assumptions…

对《暴露映射误设下的因果推断：区分定义与假设》的讨论：推断所需的干扰限制AI 解读

We congratulate the author on an insightful paper that provides a discerning critique of the use of exposure mappings in studies with interference. The main observation is that exposure mappings serve two different functions; they define the estimand and impose structure on potential outcomes by restricting interference. The first function commonly leads researchers to select simple exposure mappings that summarize treatments assigned to units ‘near’ the ego. However, due to…

利用干预实验数据贝叶斯学习网络结构AI 解读

Summary Directed acyclic graphs provide an effective framework for learning causal relationships among variables given multivariate observations. Under pure observational data, directed acyclic graphs encoding the same conditional independencies cannot be distinguished and are collected into Markov equivalence classes. In many contexts, however, observational measurements are supplemented by interventional data that improve directed acyclic graph identifiability and enhance…

无标签网络总体：图空间几何与广义测地主成分AI 解读

Abstract Statistical analysis for populations of networks is widely applicable, but challenging, as networks have strongly non-Euclidean behaviour. Graph space is an exhaustive framework for studying populations of unlabelled networks that are weighted or unweighted, uni- or multilayered, directed or undirected. Viewing graph space as the quotient of a Euclidean space with respect to a finite group action, we show that it is not a manifold, and that its curvature is…

暴露映射误设下的因果推断：分离定义与假设AI 解读

Summary Exposure mappings facilitate investigations of complex causal effects when units interact in experiments. Current methods require experimenters to use the same exposure mappings to define the effect of interest and to impose assumptions on the interference structure. However, the two roles rarely coincide in practice, and experimenters are forced to make the often questionable assumption that their exposures are correctly specified. This paper argues that the two…

用于评估自回归过程处理效应的动态合成控制法AI 解读

Abstract Motivated by evaluating the effects of air pollution alerts on air quality, we propose the dynamic synthetic control method for micro-level data with time-varying confounders and spatial dependence under an auto-regressive model setting. We employ the empirical likelihood to define the synthetic control weights, which ensures a unique solution and permits theoretical analysis. The dynamic matching increases the feasibility of matching and enables us to assess the…