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含部分可观测因子的高频因子分析AI 解读

This paper considers a novel factor structure – Partially Observable Factor Model – where both observable factors and latent factors exist in the model simultaneously. Such factor structure can make sure both interpretability and goodness-of-fit at the same time. Necessary estimation methodologies for this partially observable factor model are developed in this paper for the high frequency data. The proposed estimation methodology is robust to jumps, microstructure noise and…

基于马氏距离的最优选择再随机化渐近理论AI 解读

Rerandomization, a design that utilizes pretreatment covariates and improves their balance between different treatment groups, has received attention recently in both theory and practice. From a survey by Bruhn and McKenzie (2009), there are at least two types of rerandomization that are used in practice: the first rerandomizes the treatment assignment until covariate imbalance is below a prespecified threshold; the second randomizes the treatment assignment multiple times…

因子模型结构变化的稳健残差检验AI 解读

In this paper, we propose an easy-to-implement residual-based specification testing procedure for detecting structural changes in factor models, which is powerful against both smooth and abrupt structural changes with unknown break dates. The proposed test is robust to the over-specified number of factors, and serially and cross-sectionally correlated error processes. A new central limit theorem is given for the quadratic forms of panel data with dependence over both…

时变向量误差修正模型：估计与推断AI 解读

This paper considers a time-varying vector error-correction model that allows for different time series behaviors (e.g., unit-root and locally stationary processes) to interact with each other and co-exist. From a practical perspective, this framework can be used to estimate shifts in the predictability of non-stationary variables, and test whether economic theories hold periodically, etc. We first develop a time-varying Granger Representation Theorem, which facilitates the…

删失选择下的分布回归AI 解读

Chernozhukov, Fernández-Val, and Luo (2023, CFL (2023) hereafter) considered a distribution regression model subject to sample selection with a binary selection mechanism. In this paper, we show how to identify and estimate a semi-parametric distribution regression model subject to a censored selection rule. With censored selection, we do not need to impose the usual outcome exclusion restriction or exclusion of the level of the latent selection variable from the selection…

多层矩阵因子模型AI 解读

Large scale matrix data has been widely discovered and continuously studied in various fields recently. We propose a multilevel matrix factor model considering the existence of multi level factor structure in matrix time series. This model incorporates both global factors influencing all matrix time series and local factors confined to impact specific matrix time series. Asymptotic properties are established to ensure the consistency of our procedure for estimating factor…

非线性趋势结果的政策评价：美国新冠疫苗接种率AI 解读

ABSTRACT This paper discusses pitfalls in two way fixed effects (TWFE) regressions when the outcome variables contain nonlinear and possibly stochastic trend components. If a policy change shifts trend paths of outcome variables, TWFE estimation can distort results and invalidate inference, especially in a context of evolving policy decisions. A robust solution is proposed by allowing for dynamic club membership empirically using a relative convergence test procedure. The…

相关误差对增长脆弱性结论的挑战AI 解读

ABSTRACT We examine the impact of correlated errors on findings regarding fluctuations in US GDP growth, which are primarily driven by the lower quantiles of the distribution as predicted by financial conditions. A proposed zero‐correlation test reveals the presence of correlated residuals in the quantile regression, introducing endogeneity and raising concerns about the validity of the previous analysis. In response, we extend the model by including two additional lags of…

多维断点回归设计的操纵检验AI 解读

ABSTRACT The causal inference model for the regression discontinuity design (RDD) relies on assumptions that imply the continuity of the density of the assignment (running) variable. The test for this implication is commonly referred to as the manipulation test and is regularly reported in applied research to strengthen the design's validity. The multidimensional RDD (MRDD) extends the RDD to contexts where treatment assignment depends on several running variables. This…

面板数据政策评价方法的预测区间AI 解读

ABSTRACT We consider the inference on individual and time specific treatment effects on the treated within the framework of panel data approach for programme evaluation. We formulate the target problem as constructing prediction intervals for high‐dimensional linear regressions with weakly dependent data. Post‐LASSO OLS is used for estimation, while dependent wild bootstrap and simple residual bootstrap are used for the construction of prediction intervals. The proposed…

内生网络中二元结果的同伴效应：战略互补与从众偏好AI 解读

ABSTRACT This paper introduces a generalized model of peer effects for binary outcomes, based on a network game that accounts for strategic complementarity (influence of the number of peers that select the same action) and conformity to social norms (penalizing deviations from the average peers' action). We derive conditions for a unique Bayesian Nash equilibrium under rational expectations about peers' behavior. Unobserved individual heterogeneity affecting network…

基于随机森林的面板数据政策评价方法AI 解读

ABSTRACT It is challenging to conduct controlled experiments to assess the impacts of social policy. To address this, past studies propose a panel data approach using factor models to estimate average treatment effects. The selection of control units is a critical step to balance the goodness of fit within‐sample with the posttreatment forecasting error when the number of observed potential control units is large. In this study, we propose using random forests, an ensemble…

因子模型的张量主成分分析AI 解读

Modern empirical analysis often relies on high-dimensional panel datasets with non-negligible cross-sectional and time-series correlations. Factor models are natural for capturing such dependencies. A tensor factor model describes the $d$-dimensional panel as a sum of a reduced rank component and an idiosyncratic noise, generalizing traditional factor models for two-dimensional panels. We consider a tensor factor model corresponding to the notion of a reduced multilinear…

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一般一阶方法的逐坐标动态与普适性AI 解读

General first-order methods (GFOMs), including many variants of gradient descent and approximate message passing algorithms, constitute a broad class of iterative algorithms widely applied in modern statistical learning problems. Some GFOMs also serve as constructive proof devices, iteratively characterizing the empirical distributions of statistical estimators in the asymptotic regime of large system limits for any fixed number of iterations. This paper develops a…

使用不可靠源数据的稳健迁移学习AI 解读

This paper addresses challenges in robust transfer learning stemming from ambiguity in Bayes classifiers and weak transferable signals between the target and source distribution. We introduce a novel quantity called the ''ambiguity level'' that measures the discrepancy between the target and source regression functions, propose a simple transfer learning procedure, and establish a general theorem that shows how this new quantity is related to the transferability of learning…

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主成分回归的高维渐近性质AI 解读

We study principal components regression (PCR) in an asymptotic high-dimensional regression setting, where the number of data points is proportional to the dimension. We derive exact limiting formulas for the estimation and prediction risks, which depend in a complicated manner on the eigenvalues of the population covariance, the alignment between the population PCs and the true signal, and the number of selected PCs. A key challenge in the high-dimensional setting stems…

低秩随机矩阵特征向量的逐元素高阶分析：偏差校正、Edgeworth 展开与自助法AI 解读

Understanding the distributions of spectral estimators in low-rank random matrix models, also known as signal-plus-noise matrix models, is fundamentally important in various statistical learning problems, including network analysis, matrix denoising and matrix completion. This paper investigates the entrywise eigenvector distributions in a broad range of low-rank signal-plus-noise matrix models by establishing their higher-order accurate stochastic expansions. At a high…

几何与自适应 k 均值聚类的渐近理论AI 解读

We revisit Pollard’s classical result on consistency for k-means clustering in Euclidean space, with a focus on extensions in two directions: first, to problems where the data may come from interesting geometric settings (e.g., Riemannian manifolds, reflexive Banach spaces or the Wasserstein space); second, to problems where some parameters are chosen adaptively from the data (e.g., k-medoids or elbow-method k-means). Toward this end, we provide a general theory, which shows…

论多路主成分分析AI 解读

Multiway data are becoming more and more common. While there are many approaches to extending principal component analysis (PCA) from usual data matrices to these types of data, the conceptual differences from the usual PCA, and their methodological implications remain largely unknown. This work aims to specifically address these questions. Through a careful and rigorous treatment, we provide a better understanding of the operating characteristics of multiway PCA, develop…

“无需”统一重叠的策略学习：悲观原则与广义经验 Bernstein 不等式AI 解读

This paper studies offline policy learning, which aims at utilizing observations collected a priori (from either fixed or adaptively evolving behavior policies) to learn an optimal individualized decision rule that achieves the best overall outcomes for a given population. Existing policy learning methods rely on a uniform overlap assumption, that is, the propensities of exploring all actions for all individual characteristics must be lower bounded; the performance of the…

算法稳定性保证分布无关预测方法的训练条件覆盖AI 解读

In a supervised learning problem, given a predicted value that is the output of some trained model, how can we quantify our uncertainty around this prediction? Distribution-free predictive inference aims to construct prediction intervals around this output, with valid coverage that does not rely on assumptions on the distribution of the data or the nature of the model training algorithm. Existing methods in this area, including conformal prediction and jackknife+, offer…

固定与随机协变量下的协方差回归分析AI 解读

Covariance regression analysis is an approach to linking the covariance of responses to a set of explanatory variables X, where X can be a vector, matrix, or tensor. Most of the literature on this topic focuses on the “Fixed-X” setting and treats X as nonrandom. By contrast, treating explanatory variables X as random, namely the “Random-X” setting, is often more realistic in practice. This article aims to fill this gap in the literature on the estimation and model assessment…

中高维协变量下完全随机实验的去偏回归调整AI 解读

Completely randomized experiment is the gold standard for causal inference. When the covariate information for each experimental candidate is available, one typical way is to include them in covariate adjustments for more accurate treatment effect estimation. In this paper, we investigate this problem under the randomization-based framework, i.e., that the covariates and potential outcomes of all experimental candidates are assumed as deterministic quantities and the…

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熵型协方差模型AI 解读

In covariance matrix estimation, one of the challenges lies in finding a suitable model and an efficient estimation method. Two commonly used modelling approaches in the literature involve imposing linear restrictions on the covariance matrix or its inverse. Another approach considers linear restrictions on the matrix logarithm of the covariance matrix. In this paper, we present a general framework for linear restrictions on different transformations of the covariance…