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增广因子模型：市场风险因子验证与债券风险溢价预测AI 解读

We study factor models augmented by observed covariates that have explanatory powers on the unknown factors. In financial factor models, the unknown factors can be reasonably well explained by a few observable proxies, such as the Fama–French factors. In diffusion index forecasts, identified factors are strongly related to several directly measurable economic variables such as consumption-wealth variable, financial ratios, and term spread. With those covariates, both the…

纵向数据分位数回归系数函数的参数化建模AI 解读

In ordinary quantile regression, quantiles of different order are estimated one at a time. An alternative approach, which is referred to as quantile regression coefficients modeling (qrcm), is to model quantile regression coefficients as parametric functions of the order of the quantile. In this article, we describe how the qrcm paradigm can be applied to longitudinal data. We introduce a two-level quantile function, in which two different quantile regression models are used…

指数族嵌入及其在单细胞 RNA 测序发育轨迹分析中的应用AI 解读

Scientists often embed cells into a lower-dimensional space when studying single-cell RNA-seq data for improved downstream analyses such as developmental trajectory analyses, but the statistical properties of such nonlinear embedding methods are often not well understood. In this article, we develop the exponential-family SVD (eSVD), a nonlinear embedding method for both cells and genes jointly with respect to a random dot product model using exponential-family…

多个脑功能网络的贝叶斯联合建模AI 解读

Investigating the similarity and changes in brain networks under different mental conditions has become increasingly important in neuroscience research. A standard separate estimation strategy fails to pool information across networks and hence has reduced estimation accuracy and power to detect between-network differences. Motivated by a fMRI Stroop task experiment that involves multiple related tasks, we develop an integrative Bayesian approach for jointly modeling…

两阶段随机实验中存在干扰与不依从时的因果推断AI 解读

In many social science experiments, subjects often interact with each other and as a result one unit’s treatment influences the outcome of another unit. Over the last decade, a significant progress has been made toward causal inference in the presence of such interference between units. Researchers have shown that the two-stage randomization of treatment assignment enables the identification of average direct and spillover effects. However, much of the literature has assumed…

网络观察性研究中处理效应与干扰效应的识别和估计AI 解读

Laura Forastierea , Edoardo M. Airoldib* & Fabrizia Meallica Department of Biostatistics, Yale University, New Haven, CTb Department of Statistical Science, Fox School of Business, Temple University, Philadelphia, PAc Department of Statistics, Informatics, Applications “G. Parenti”, University of Florence, Florence, ItalySupplementary materials for this article are available online. Please go to www.tandfonline.com/r/JASA.CONTACT Edoardo M. Airoldi airoldi@temple.edu…

处理效应分析中联合分布泛函的尖锐界AI 解读

This article proposes an identification and estimation method that allows researchers to bound continuous functionals of the joint distribution of potential outcomes from the literature on treatment effects. The focus is on a model where no restrictions are imposed on treatment selection. The method can sharply bound interesting parameters when analytical bounds are difficult to derive, can be used in settings in which instruments are available, and can easily accommodate…

区分个体处理效应与溢出效应的框架AI 解读

This article suggests a causal framework for separating individual-level treatment effects and spillover effects such as general equilibrium, interference, or interaction effects related to treatment distribution. We relax the stable unit treatment value assumption assuming away treatment-dependent interaction between study participants and permit spillover effects within aggregates, for example, regions. Based on our framework, we systematically categorize the…

不依赖偏好相似性限制的集体家庭随机资源份额识别AI 解读

Resource shares, defined as the fraction of total household spending going to each person in a household, are important for assessing individual material well-being, inequality, and poverty. They are difficult to identify because consumption is measured typically at the household level, and many goods are jointly consumed, so that individual level consumption in multi-person households is not directly observed. We consider random resource shares, which vary across…

面板数据单指标模型中的同质结构识别AI 解读

Panel data analysis is an important topic in statistics and econometrics. Traditionally, in panel data analysis, all individuals are assumed to share the same unknown parameters, e.g. the same coefficients of covariates when the linear models are used, and the differences between the individuals are accounted for by cluster effects. This kind of modelling only makes sense if our main interest is on the global trend, this is because it would not be able to tell us anything…

利用工具变量估计最优治疗规则：部分识别学习方法AI 解读

Abstract Individualized treatment rules (ITRs) are considered a promising recipe to deliver better policy interventions. One key ingredient in optimal ITR estimation problems is to estimate the average treatment effect conditional on a subject’s covariate information, which is often challenging in observational studies due to the universal concern of unmeasured confounding. Instrumental variables (IVs) are widely used tools to infer the treatment effect when there is…

锚回归：异质数据与因果性AI 解读

Abstract We consider the problem of predicting a response variable from a set of covariates on a data set that differs in distribution from the training data. Causal parameters are optimal in terms of predictive accuracy if in the new distribution either many variables are affected by interventions or only some variables are affected, but the perturbations are strong. If the training and test distributions differ by a shift, causal parameters might be too conservative to…

高维均值向量变点的有限样本推断与定位AI 解读

Abstract Cumulative sum (CUSUM) statistics are widely used in the change point inference and identification. For the problem of testing for existence of a change point in an independent sample generated from the mean-shift model, we introduce a Gaussian multiplier bootstrap to calibrate critical values of the CUSUM test statistics in high dimensions. The proposed bootstrap CUSUM test is fully data dependent and it has strong theoretical guarantees under arbitrary dependence…

利用图根分布表示网络AI 解读

Exchangeable random graphs serve as an important probabilistic framework for the statistical analysis of network data. In this work, we develop an alternative parameterization for a large class of exchangeable random graphs, where the nodes are independent random vectors in a linear space equipped with an indefinite inner product, and the edge probability between two nodes equals the inner product of the corresponding node vectors. Therefore, the distribution of exchangeable…

扩散Copula：识别与估计AI 解读

We propose a new semiparametric approach for modelling nonlinear univariate diffusions, where the observed process is a nonparametric transformation of an underlying parametric diffusion (UPD). This modelling strategy yields a general class of semiparametric Markov diffusion models with parametric dynamic copulas and nonparametric marginal distributions. We provide primitive conditions for the identification of the UPD parameters together with the unknown transformations…

含主导单元的空间模型估计与推断AI 解读

In spatial econometrics literature estimation and inference are carried out assuming that the matrix of spatial or network connections has uniformly bounded absolute column sums in the number of units, n, in the network. This paper relaxes this restriction and allows for one or more units to have pervasive effects in the network. The linear–quadratic central limit theorem of Kelejian and Prucha (2001) is generalized to allow for such dominant units, and the asymptotic…

大型面板模型中广泛影响单元的检测AI 解读

The importance of units that influence a large number of other units in a network has become increasingly recognized in the literature. In this paper we propose a new method to detect such pervasive units by basing our analysis on unit-specific residual error variances subject to suitable adjustments due to the multiple testing issues involved. Accordingly, a sequential multiple testing (SMT) procedure is proposed, which allows identification of pervasive units (if any)…

非参数部分识别下部分线性工具变量模型的稳健与最优估计AI 解读

This paper studies robust and optimal estimation of the slope coefficients in a partially linear instrumental variables model with nonparametric partial identification. We establish the root-n asymptotic normality of a penalized sieve minimum distance estimator of the slope coefficients. We show that the asymptotic normality holds regardless of whether the nonparametric function is point identified or only partially identified. However, in the presence of nonparametric…

动态面板空间向量自回归估计：稳定性与空间多变量协整AI 解读

This paper introduces dynamic panel spatial vector autoregressive models. We study features of dynamics and spatial interactions that an SVAR model can generate and classify the model into stable or unstable cases by partitioning parameter spaces. For stable, spatial cointegration, and mixed cointegration cases, we investigate identification and QML estimation of the models to take into account simultaneity and correlated relationships. Asymptotic properties and…

异方差与多工具变量下的工具变量模型推断AI 解读

This paper proposes novel inference procedures for instrumental variable models in the presence of many, potentially weak instruments that are robust to the presence of heteroskedasticity. First, we provide an Anderson–Rubin-type test for the entire parameter vector that is valid under assumptions weaker than previously proposed Anderson–Rubin-type tests. Second, we consider the case of testing a subset of parameters under the assumption that a consistent estimator for the…

一类指数模型的识别：拓扑方法AI 解读

Summary We establish nonparametric identification in a class of so-called index models by using a novel approach that relies on general topological results. Our proof strategy requires substantially weaker conditions on the functions and distributions characterising the model than those required by existing strategies; in particular, it does not require any large-support conditions on the regressors of our model. We apply the general identification result to additive random…

结构向量自回归模型中异方差识别的检验AI 解读

Summary Tests for identification through heteroskedasticity in structural vector autoregressive analysis are developed for models with two volatility states where the time point of volatility change is known. The tests are Wald-type tests for which only the unrestricted model, including the covariance matrices of the two volatility states, has to be estimated. The residuals of the model are assumed to be from the class of elliptical distributions, which includes Gaussian…

条件分位数限制识别的非参数工具变量模型中的外生性检验AI 解读

Summary This paper presents a test for exogeneity of explanatory variables in a nonparametric instrumental variables (IV) model whose structural function is identified through a conditional quantile restriction. Quantile regression models are increasingly important in applied econometrics. As with mean-regression models, an erroneous assumption that the explanatory variables in a quantile regression model are exogenous can lead to highly misleading results. In addition, a…

高维经验似然推断AI 解读

Summary High-dimensional statistical inference with general estimating equations is challenging and remains little explored. We study two problems in the area: confidence set estimation for multiple components of the model parameters, and model specifications tests. First, we propose to construct a new set of estimating equations such that the impact from estimating the high-dimensional nuisance parameters becomes asymptotically negligible. The new construction enables us to…