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深度复合空间模型AI 解读

Spatial processes with nonstationary and anisotropic covariance structure are often used when modeling, analyzing, and predicting complex environmental phenomena. Such processes may often be expressed as ones that have stationary and isotropic covariance structure on a warped spatial domain. However, the warping function is generally difficult to fit and not constrained to be injective, often resulting in “space-folding.” Here, we propose modeling an injective warping…

利用参数化决策分析实现快速、最优与目标导向预测AI 解读

Prediction is critical for decision-making under uncertainty and lends validity to statistical inference. With targeted prediction, the goal is to optimize predictions for specific decision tasks of interest, which we represent via functionals. Although classical decision analysis extracts predictions from a Bayesian model, these predictions are often difficult to interpret and slow to compute. Instead, we design a class of parameterized actions for Bayesian decision…

含协变量的双成分混合模型AI 解读

In this article, we study a generalization of the two-groups model in the presence of covariates—a problem that has recently received much attention in the statistical literature due to its applicability in multiple hypotheses testing problems. The model we consider allows for infinite dimensional parameters and offers flexibility in modeling the dependence of the response on the covariates. We discuss the identifiability issues arising in this model and systematically study…

主成分分析的分布式估计：扩展特征子空间分析AI 解读

The growing size of modern datasets brings many challenges to the existing statistical estimation approaches, which calls for new distributed methodologies. This article studies distributed estimation for a fundamental statistical machine learning problem, principal component analysis (PCA). Despite the massive literature on top eigenvector estimation, much less is presented for the top-L-dim (L > 1) eigenspace estimation, especially in a distributed manner. We propose a…

Wald 检验中的 Hauck–Donner 效应：检测、临界点与参数空间刻画AI 解读

The Wald test remains ubiquitous in statistical practice despite shortcomings such as its inaccuracy in small samples and lack of invariance under reparameterization. This article develops on another but lesser-known shortcoming called the Hauck–Donner effect (HDE) whereby a Wald test statistic is no longer monotone increasing as a function of increasing distance between the parameter estimate and the null value. Resulting in an upward biased p-value and loss of power, the…

含噪、稀疏及片段化函数数据的同步配准与估计：贝叶斯框架AI 解读

In many applications, smooth processes generate data that is recorded under a variety of observational regimes, including dense sampling and sparse or fragmented observations that are often contaminated with error. The statistical goal of registering and estimating the individual underlying functions from discrete observations has thus far been mainly approached sequentially without formal uncertainty propagation, or in an application-specific manner by pooling information…

结合网络嵌入的有向社区检测AI 解读

Community detection in network data aims at grouping similar nodes sharing certain characteristics together. Most existing methods focus on detecting communities in undirected networks, where similarity between nodes is measured by their node features and whether they are connected. In this article, we propose a novel method to conduct network embedding and community detection simultaneously in a directed network. The network embedding model introduces two sets of vectors to…

结构化收缩估计的回归建模方法AI 解读

Problems involving the simultaneous estimation of multiple parameters arise in many areas of theoretical and applied statistics. A canonical example is the estimation of a vector of normal means. Frequently, structural information about relationships between the parameters of interest is available. For example, in a gene expression denoising problem, genes with similar functions may have similar expression levels. Despite its importance, structural information has not been…

评 Giacomini、Kitagawa 与 Read 的《叙事限制与代理变量》AI 解读

In a series of recent studies, Raffaella Giacomini and Toru Kitagawa have developed an innovative methodological approach to estimating sign-identified structural VAR models that seeks to build a bridge between Bayesian and frequentist approaches in the literature. Their latest papers with Matthew Read contain thought-provoking new insights about modeling narrative restrictions in sign-identified structural VAR models. My discussion puts their contribution into the context…

对《叙事限制与代理变量》的讨论AI 解读

I am grateful for the chance to discuss this characteristically insightful paper by Giacomini, Kitagawa, and Read (hence-forth GKR). Since the seminal contribution of Antolín-Díaz and Rubio-Ramírez (2018), narrative restrictions have rapidly become one of the go-to tools for sharpening causal inference in SVAR analysis. Giacomini, Kitagawa, and Read (2021) con-tributed greatly to our understanding of the role of subjective prior beliefs and the appropriate form of the…

断点回归中的局部复合分位数回归AI 解读

We introduce the local composite quantile regression (LCQR) to causal inference in regression discontinuity (RD) designs. Kai, Li and Zou study the efficiency property of LCQR, while we show that its nice boundary performance translates to accurate estimation of treatment effects in RD under a variety of data generating processes. Moreover, we propose a bias-corrected and standard error-adjusted t-test for inference, which leads to confidence intervals with good coverage…

连续处理效应模型设定检验的统一框架AI 解读

We propose a general framework for the specification testing of continuous treatment effect models. We assume a general residual function, which includes the average and quantile treatment effect models as special cases. The null models are identified under the unconfoundedness condition and contain a nonparametric weighting function. We propose a test statistic for the null model in which the weighting function is estimated by solving an expanding set of moment equations.…

交互效应面板模型的变换估计AI 解读

We propose a transformed estimator for the slope coefficients of panel models with interactive effects. The transformed estimation method does not require the prior knowledge of the dimension of factor structure. It is consistent and asymptotically normally distributed under fairly general conditions when N is fixed and T→∞ or T is fixed and N→∞, or when both N and T are large and NT→a≠0<∞. Moreover, because the transformation is equivalent to aggregating cross-sectional…

不施加均衡限制的博弈推断：餐厅营业时间竞争应用AI 解读

This article relaxes the Bayesian Nash equilibrium assumption in the estimation of discrete choice games with incomplete information. Instead of assuming unbiased/correct expectations, the model specifies a player’s belief about the behaviors of other players as an unrestricted unknown function. I then study the joint identification of belief and payoff functions in a game where players have different numbers of actions (e.g., 3 × 2 game). This asymmetry in action sets…

后验平均效应AI 解读

Economists are often interested in estimating averages with respect to distributions of unobservables, such as moments of individual fixed-effects, or average partial effects in discrete choice models. For such quantities, we propose and study posterior average effects (PAE), where the average is computed conditional on the sample, in the spirit of empirical Bayes and shrinkage methods. While the usefulness of shrinkage for prediction is well-understood, a justification of…

含固定效应与时变系数的半参数空间自回归面板模型AI 解读

This article considers a semiparametric spatial autoregressive (SAR) panel data model with fixed effects and time-varying coefficients. The time-varying coefficients are allowed to follow unknown functions of time, while the other parameters are assumed to be unknown constants. We propose a local linear quasi-maximum likelihood estimation method to obtain consistent estimators for the SAR coefficient, the variance of the error term, and the nonparametric time-varying…

马尔可夫转换 GARCH 模型：高阶矩、峰度与衰退及疫情期间的波动评估AI 解读

In this article, we derive neat matrix formulas in closed form for computing higher order moments and kurtosis of univariate Markov switching GARCH models. Then we provide asymptotic theory for sample estimators of higher order moments and kurtosis which can be used for testing normality. We also check our theory statements numerically via Monte Carlo simulations. Finally, we take advantage of our theoretical results to recognize different periods of high volatility…

使用 CCE 估计因子的嵌套模型等预测准确性检验AI 解读

In this article, we propose new tests of equal predictive ability between nested models when factor-augmented regressions are used to forecast. In contrast to the previous literature, the unknown factors are not estimated by principal components but by the common correlated effects (CCE) approach, which employs cross-sectional averages of blocks of variables. This makes for easy interpretation of the estimated factors, and the resulting tests are easy to implement and they…

工具变量下局部平均处理效应的高维模型辅助推断AI 解读

Consider the problem of estimating the local average treatment effect with an instrument variable, where the instrument unconfoundedness holds after adjusting for a set of measured covariates. Several unknown functions of the covariates need to be estimated through regression models, such as instrument propensity score and treatment and outcome regression models. We develop a computationally tractable method in high-dimensional settings where the numbers of regression terms…

变系数中介模型及其在行为经济学数据分析中的应用AI 解读

This paper is concerned with causal mediation analysis with varying indirect and direct effects. We propose a varying coefficient mediation model, which can also be viewed as an extension of moderation analysis on a causal diagram. We develop a new estimation procedure for the direct and indirect effects based on B-splines. Under mild conditions, rates of convergence and asymptotic distributions of the resulting estimates are established. We further propose a F-type test for…

处理变量缺失或误测时的局部平均处理效应AI 解读

We provide a new estimator, MR-LATE, that consistently estimates local average treatment effects when treatment is missing for some observations, not at random. If instead treatment is mismeasured for some observations, then MR-LATE usually has less bias than the standard LATE estimator. We discuss potential applications where an endogenous binary treatment may be unobserved or mismeasured. We apply MR-LATE to study the impact of women’s control over household resources on…

先导抽样与一步更新的分布式分位数回归札记AI 解读

Quantile regression is a method of fundamental importance. How to efficiently conduct quantile regression for a large dataset on a distributed system is of great importance. We show that the popularly used one-shot estimation is statistically inefficient if data are not randomly distributed across different workers. To fix the problem, a novel one-step estimation method is developed with the following nice properties. First, the algorithm is communication efficient. That is…

可能存在多次周期结构变化的非平稳时间序列稳健推断AI 解读

Motivated by two examples concerning global warming and monthly total import and export by China, we study time series that contain a nonparametric periodic component with an unknown period, a nonparametric trending behavior and also additive covariate effects. Further, as the amplitude function may change at some known or unknown change-point(s), we extend our model to take this dynamical periodicity into account and introduce two change-point estimators. To the best of…

多项 Probit 模型中的可扩展贝叶斯估计AI 解读

The multinomial probit (MNP) model is a popular tool for analyzing choice behavior as it allows for correlation between choice alternatives. Because current model specifications employ a full covariance matrix of the latent utilities for the choice alternatives, they are not scalable to a large number of choice alternatives. This article proposes a factor structure on the covariance matrix, which makes the model scalable to large choice sets. The main challenge in estimating…