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美国银行间流动性风险网络的情景分位数连通性AI 解读

We characterize the U.S. interbank liquidity risk network based on a supervisory dataset, using a scenario-based quantile network connectedness approach. In terms of methodology, we consider a quantile vector autoregressive model with unobserved heterogeneity and propose a Bayesian nuclear norm estimation method. A common factor structure is employed to deal with unobserved heterogeneity that may exhibit endogeneity within the network. Then we develop a scenario-based…

This paper investigates the stability of deep ReLU neural networks for nonparametric regression under the assumption that the noise has only a finite p-th moment. We unveil how the optimal rate of convergence depends on p, the degree of smoothness and the intrinsic dimension in a class of nonparametric regression functions with hierarchical composition structure when both the adaptive Huber loss and deep ReLU neural networks are used. This optimal rate of convergence cannot…

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基于多重测量的线性随机偏微分方程最优参数估计AI 解读

The coefficients in a second order parabolic linear stochastic partial differential equation (SPDE) are estimated from multiple spatially localised measurements. Assuming that the spatial resolution tends to zero and the number of measurements is nondecreasing, the rate of convergence for each coefficient depends on its differential order and is faster for higher order coefficients. Based on an explicit analysis of the reproducing kernel Hilbert space of a general stochastic…

不完全信息离散选择模型中的识别AI 解读

We study identification of preferences in static single-agent discrete choice models where decision makers may be imperfectly informed about the state of the world. Leveraging the notion of one-player Bayes Correlated Equilibrium by Bergemann and Morris (2016), we provide a tractable characterisation of the sharp identified set. We develop a procedure to practically construct the sharp identified set following a sieve approach, and provide sharp bounds on counterfactual…

门槛空间自回归模型AI 解读

In this paper, we consider the estimation and inferential issues of the threshold spatial autoregressive (TSAR) model, which is a hybrid of the threshold and spatial autoregressive models. We use the quasi maximum likelihood (QML) method to estimate the model. In addition, we prove the tightness and the Hájek–Rényi type inequality for a quadratic form and establish a full inferential theory of the QML estimator under the setup that threshold effect shrinks to zero as the…

含动态潜在因子的小维度 VAR 模型中的工具变量推断AI 解读

We study semiparametric inference in a small-dimensional vector autoregressive (VAR) model of order p augmented by unobservable common factors with a dynamic described by a VAR process of order q . This state-space specification is useful to measure separately the direct causality effects and the responses to dynamic common factors. We show that the state-space parameters are identifiable from the autocovariance function of the observed process. We estimate the model by…

从极端数据估计指向根节点的有向树AI 解读

Abstract We propose a new method to estimate a root-directed spanning tree from extreme data. Prominent example is a river network, to be discovered from extreme flow measured at a set of stations. Our new algorithm utilizes qualitative aspects of a max-linear Bayesian network, which has been designed for modelling causality in extremes. The algorithm estimates bivariate scores and returns a root-directed spanning tree. It performs extremely well on benchmark data and on new…

Abstract The aim of this article is to establish a causal link between the policies implemented by technology companies and the outcomes they yield within intricate temporal and/or spatial dependent experiments. We propose a novel temporal/spatio-temporal Varying Coefficient Decision Process model, capable of effectively capturing the evolving treatment effects in situations characterized by temporal and/or spatial dependence. Our methodology encompasses the decomposition of…

动态条件分位数处理效应的评估及网约车应用AI 解读

Many modern tech companies, such as Google, Uber, and Didi, utilize online experiments (also known as A/B testing) to evaluate new policies against existing ones. While most studies concentrate on average treatment effects, situations with skewed and heavy-tailed outcome distributions may benefit from alternative criteria, such as quantiles. However, assessing dynamic quantile treatment effects (QTE) remains a challenge, particularly when dealing with data from ride-sourcing…

结合感染调查与病例计数辅助信息估计奥地利新冠感染率AI 解读

Countries officially record the number of COVID-19 cases based on medical tests of a subset of the population. These case count data obviously suffer from participation bias, and for prevalence estimation, these data are typically discarded in favour of infection surveys, or possibly also completed with auxiliary information. One exception is the series of infection surveys recorded by the Statistics Austria Federal Institute to study the prevalence of COVID-19 in Austria in…

空间转录组研究中的贝叶斯整合区域分割AI 解读

The spatially resolved transcriptomic study is a recently developed biological experiment that can measure gene expressions and retain spatial information simultaneously, opening a new avenue to characterize fine-grained tissue structures. In this article, we propose a nonparametric Bayesian method named BINRES to carry out the region segmentation for a tissue section by integrating all the three types of data generated during the study—gene expressions, spatial coordinates,…

理想的贝叶斯局部自适应AI 解读

Many real-life applications involve estimation of curves that exhibit complicated shapes including jumps or varying-frequency oscillations. Practical methods have been devised that can adapt to a locally varying complexity of an unknown function (e.g. variable-knot splines, sparse wavelet reconstructions, kernel methods or trees/forests). However, the overwhelming majority of existing asymptotic minimaxity theory is predicated on homogeneous smoothness assumptions. Focusing…

群组网络Hawkes过程AI 解读

In this work, we study the event occurrences of individuals interacting in a network. To characterize the dynamic interactions among the individuals, we propose a group network Hawkes process (GNHP) model whose network structure is observed and fixed. In particular, we introduce a latent group structure among individuals to account for the heterogeneous user-specific characteristics. A maximum likelihood approach is proposed to simultaneously cluster individuals in the…

不规则测量数据下的高斯图模型推断AI 解读

In this article, we investigate the Gaussian graphical model inference problem in a novel setting that we call erose measurements, referring to irregularly measured or observed data. For graphs, this results in different node pairs having vastly different sample sizes which frequently arises in data integration, genomics, neuroscience, and sensor networks. Existing works characterize the graph selection performance using the minimum pairwise sample size, which provides…

离散难计算似然的广义贝叶斯推断AI 解读

Discrete state spaces represent a major computational challenge to statistical inference, since the computation of normalization constants requires summation over large or possibly infinite sets, which can be impractical. This article addresses this computational challenge through the development of a novel generalized Bayesian inference procedure suitable for discrete intractable likelihood. Inspired by recent methodological advances for continuous data, the main idea is to…

基于随机生成树的非平稳软分区高斯过程模型AI 解读

There has been a long-standing challenge in developing locally stationary Gaussian process models concerning how to obtain flexible partitions and make predictions near boundaries. In this work, we develop a new class of locally stationary stochastic processes, where local partitions are modeled by a soft partition process via predictive random spanning trees that leads to highly flexible spatially contiguous subregion shapes. This valid nonstationary process model knits…

网络数据的经验似然AI 解读

This article develops a concept of nonparametric likelihood for network data based on network moments, and proposes general inference methods by adapting the theory of jackknife empirical likelihood. Our methodology can be used not only to conduct inference on population network moments and parameters in network formation models, but also to implement goodness-of-fit testing, such as testing block size for stochastic block models. Theoretically we show that the jackknife…

PCABM：用于社区检测的成对协变量调整块模型AI 解读

One of the most fundamental problems in network study is community detection. The stochastic block model (SBM) is a widely used model, and various estimation methods have been developed with their community detection consistency results unveiled. However, the SBM is restricted by the strong assumption that all nodes in the same community are stochastically equivalent, which may not be suitable for practical applications. We introduce a pairwise covariates-adjusted stochastic…

FNETS：高维时间序列的因子调整网络估计与预测AI 解读

–We propose FNETS, a methodology for network estimation and forecasting of high-dimensional time series exhibiting strong serial- and cross-sectional correlations. We operate under a factor-adjusted vector autoregressive (VAR) model which, after accounting for pervasive co-movements of the variables by common factors, models the remaining idiosyncratic dynamic dependence between the variables as a sparse VAR process. Network estimation of FNETS consists of three steps: (i)…

忽略社会网络中的测量误差AI 解读

Summary We consider peer effect estimation in social network models where some network links are incorrectly measured. We show that if the number or magnitude of mismeasured links does not grow too quickly with the sample size, then standard instrumental variables estimators that ignore these measurement errors remain consistent, and standard asymptotic inference methods remain valid. These results hold even when the link measurement errors are correlated with regressors or…

存在地点特定不可观测局部效应的样本选择模型：空间差分方法AI 解读

Summary This paper proposes an estimator which combines spatial differencing with a two-step sample selection estimator. We derive identification, estimation, and inference results from ‘site-specific’ unobserved effects. These effects operate at a spatial scale that cannot be captured by administrative borders. Therefore, we use spatial differencing. We show that under justifiable assumptions, the estimator is consistent and asymptotically normal. A Monte Carlo experiment…

基于多个小网络的随机区块模型非参数识别与估计AI 解读

This paper concerns the analysis of network data when unobserved node-specific heterogeneity is present. We postulate a weighted version of the classic stochastic block model, where nodes belong to one of a finite number of latent communities and the placement of edges between them and any weight assigned to these depend on the communities to which the nodes belong. A simple rank condition is presented under which we establish that the number of latent communities, their…

空间计量模型的最优线性与二次矩：美国县域就业增长应用AI 解读

Summary We provide a novel analytic procedure to construct best linear and quadratic moments of the generalized method of moments estimation for a large class of cross‐sectional network and spatial econometric models. These moments generate an estimator that is asymptotically more efficient than the quasi‐maximum likelihood estimator when the disturbances follow a non‐normal and unknown distribution. We apply this procedure to a high‐order spatial autoregressive model with…

横截面乘数估计值得信赖吗？AI 解读

Summary I examine the properties of cross‐sectional estimators of multipliers, elasticities, or pass‐throughs when a conventional spatial macroeconomic specification generates the data. A number of important biases plague standard estimates; the most relevant one occurs when the units display heterogeneous dynamics. Methods that work well in this situation are suggested. An experimental setting shows the magnitude of the biases cross‐sectional estimators display. Average…