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有效尾部假说：市场效率的极值视角AI 解读

In finance, the Efficient Market Hypothesis posits that asset prices reflect all available information in the market. Several empirical investigations show that market efficiency drops when it undergoes extreme events. Many models for multivariate extremes focus on positive dependence, making them unsuitable for studying extremal dependence in financial markets where data often exhibit both positive and negative extremal dependence. To this end, we construct regular…

金融研究中的时变分组不可观测异质性AI 解读

Accounting for time-varying unobserved heterogeneity poses a fundamental challenge for empirical finance research. We show how grouped fixed effects (GFE) models capture such heterogeneity and illustrate their merits over conventional panel data models used in finance applications. We propose a new Hausman-type specification test to select among fixed effects models, and show that it differs from standard Hausman statistics precisely due to the presence of the time-varying…

多维聚类与序列相关时间效应下的野生自助法推断AI 解读

This article studies wild bootstrap-based inference for regression models with multiway clustering. Our proposed methods are multiway counterparts to the (one-way) wild cluster bootstrap approach introduced by Cameron, Gelbach, and Miller. We establish the validity of our methods for studentized statistics. Theoretical results are provided, accommodating arbitrary serial dependence in the common time effects—an aspect excluded by existing two-way bootstrap-based approaches.…

广义泊松伪极大似然估计量AI 解读

We examine the Constant Variance to Mean Ratio (CVMR) assumption – a key condition to make PPML an efficient estimator – and propose Generalized Poisson-Pseudo Maximum Likelihood (G-PPML) as a complementary estimator. We estimate the conditional variance of the dependent variable using an iterated GMM, thereby providing a specification test for the CVMR assumption. The proposed G-PPML estimator, which capitalizes on conditional variance estimates, is more efficient than…

时变参数 VAR 中与经济理论一致的收缩AI 解读

This article introduces a theory coherent shrinkage prior for Time-Varying Parameter VARs (TVP-VARs). The prior centers the time-varying parameters on a path implied a priori by an underlying economic theory, chosen to describe the dynamics of the macroeconomic variables in system. Leveraging information from conventional economic theory using this prior significantly improves inference precision and forecast accuracy compared to the standard TVP-VAR. In an application, I…

双重稳健的交错双重差分组别—时期条件平均处理效应一致置信带AI 解读

We consider a panel data analysis to examine the heterogeneity in treatment effects with respect to groups, periods, and a pre-treatment covariate of interest in the staggered difference-in-differences setting of Callaway and Sant’Anna. Under standard identification conditions, a doubly robust estimand conditional on the covariate identifies the group-time conditional average treatment effect given the covariate. Focusing on the case of a continuous covariate, we propose a…

随机权重矩阵与解释变量下空间自回归模型的拟得分匹配估计AI 解读

With the rapid advancements in technology for data collection, the application of the spatial autoregressive (SAR) model has become increasingly prevalent in real-world analysis, particularly when dealing with large datasets. However, the commonly used quasi-maximum likelihood estimation (QMLE) for the SAR model is not computationally scalable to handle the data with a large size. In addition, when establishing the asymptotic properties of the parameter estimators of the SAR…

包络矩阵自回归模型AI 解读

Matrix-valued data is commonly collected over time in many scientific fields. However, existing methods for handling such data are limited and often suffer from overparametrization. In response, Chen et al. (2021) introduced the matrix autoregressive (MAR) model as an alternative to traditional time series analysis, which relies on vectorization and vector autoregression frameworks. By preserving the original structure of matrices, the MAR model avoids the loss of valuable…

内生性下的极端分位数处理效应AI 解读

We introduce a novel method for estimating and conducting inference about extreme quantile treatment effects (QTEs) in the presence of endogeneity. Our approach is applicable to a broad range of empirical research designs, including instrumental variables design and regression discontinuity design, among others. By leveraging regular variation and subsampling, the method ensures good performance even in extreme tails, where data may be sparse. Simulation studies confirm the…

响应缺失空间面板模型的固定效应估计：美国州税收竞争应用AI 解读

We consider estimation and inferences for general spatial panel data models with randomly missing observations on responses. It allows for unobserved spatiotemporal heterogeneity, time-varying endogenous and contextual spatial interactions, time-varying cross-sectional error dependence, and serial correlation. A general M-estimation method is proposed for model estimation and a novel corrected plug-in method is proposed for model inference. Both take into account the…

小簇样本下聚类数据分析的同质性寻求AI 解读

Clustered data analysis is an important topic in data science. A well established approach is to assume all clusters share the same unknown parameters of interest, and the difference between different clusters is formulated and accounted for by cluster effects. Whilst this approach works very well in many issues, such as exploring the global impact of an explanatory variable on the response variable, it does not provide much insight about individual attributes of each…

可解释的市场微观结构噪声AI 解读

High-frequency financial data are often contaminated by market microstructure effects. In this study, we consider a setting where a portion of the microstructure noise can be explained by observable trading information, referred to as the explicative noise component. To formally analyze this component, we first develop a model-free variable importance measure in the high-frequency setting that quantifies the price impact of subsets of trading variables. Based on the…

通过变分自编码器快速灵活地模拟空间极值过程AI 解读

Many real-world processes have complex tail dependence structures that cannot be adequately characterized using classical Gaussian processes. Alternatively, models motivated by extreme-value theory exhibit appealing extremal dependence properties but are often exceedingly prohibitive to fit and simulate from in high dimensions using classical methods. In this paper, we extend the boundaries on computation and modeling of high-dimensional spatial extremes by integrating a new…

通过随机梯度下降对情境老虎机进行在线统计推断AI 解读

With the fast development of big data, learning the optimal decision rule by recursively updating it and making online decisions has been easier than before. We study the online statistical inference of model parameters in a contextual bandit framework of sequential decision-making. We propose a general framework for an online and adaptive data collection environment that can update decision rules via weighted stochastic gradient descent. We allow different weighting schemes…

控制错误发现率的局部关联搜索AI 解读

We introduce local conditional hypotheses that express how the relation between explanatory variables and outcomes changes across different contexts, described by covariates. By expanding upon the model-X knockoff filter, we show how to adaptively discover these local associations, all while controlling the false discovery rate. Our enhanced inferences can help explain sample heterogeneity and uncover interactions, making better use of the capabilities offered by modern…

利用深度神经网络对删失数据进行非参数推断AI 解读

Abstract We propose a novel deep learning approach to nonparametric statistical inference for the conditional hazard function of survival time with right-censored data. We use a deep neural network (DNN) to approximate the logarithm of a conditional hazard function given covariates and obtain a DNN likelihood-based estimator of the conditional hazard function. Such an estimation approach enhances model flexibility and hence relaxes structural and functional assumptions on…

重探伪逆：高维 Moore–Penrose 逆与岭型逆的渐近性质及应用AI 解读

In this paper, we derive high-dimensional asymptotic properties of the Moore–Penrose inverse and, as a byproduct, of various ridge-type inverses of the sample covariance matrix. In particular, the analytical expressions of the asymptotic behavior of the weighted sample trace moments of generalized inverse matrices are deduced in terms of the partial exponential Bell polynomials, which can be easily computed in practice. The existent results for pseudo-inverses are extended…

高维样本协方差矩阵的广义线性谱统计及其应用AI 解读

In this paper, we introduce the Generalized Linear Spectral Statistics (GLSS) of a high-dimensional sample covariance matrix Sn, denoted as trf(Sn)Bn, which effectively captures distinct spectral properties of Sn by incorporating an ancillary matrix Bn and a test function f. The joint asymptotic normality of GLSS associated with different test functions is established under mild assumptions on Bn and the underlying distribution, when the dimension n and sample size N are…

LASSO 及相关估计量的分布鲁棒预测误差AI 解读

We study the classical problem of predicting an outcome variable, Y, using a linear combination of a d-dimensional covariate vector, X. We are interested in linear predictors whose coefficients solve infβ∈Rd(EPn[|Y−X⊤β|r])1/r+δρ(β), where δ>0 is a regularization parameter, ρ:Rd→R+ is a convex penalty function, Pn is the empirical distribution of the data and r≥1. Our main contribution is a new bound on the out-of-distribution prediction error of such estimators. The new…

张量预测变量充分降维的广义多线性模型AI 解读

We consider supervised learning problems with tensor-valued input. We derive multilinear sufficient reductions for the regression or classification problem by modeling the conditional distribution of the predictors given the response as a member of the quadratic exponential family. We develop estimation procedures of sufficient reductions for both continuous and binary tensor-valued predictors. We prove the consistency and asymptotic normality of the estimated sufficient…

利用再现样本进行高维线性回归模型与系数的有限样本及大样本推断AI 解读

In this paper, we present a novel and effective inference approach to conduct both finite and large sample inference for high-dimensional linear regression models. This approach is developed under the so-called repro samples framework, in which we conduct statistical inference by creating and studying the behavior of artificial samples that are obtained by mimicking the sampling mechanism of the data. We construct confidence sets for (a) the true model corresponding to the…

正则化M估计量装袋集成的精确渐近分析AI 解读

We characterize the squared prediction risk of ensemble estimators obtained through subagging (subsample bootstrap aggregating) regularized M-estimators and construct a consistent estimator for the risk. Specifically, we consider a heterogeneous collection of M≥1 regularized M-estimators, each trained with (possibly different) subsample sizes, convex differentiable losses, and convex regularizers. We operate under the proportional asymptotics regime, where the sample size n,…

再生核希尔伯特空间中非参数随机逼近的可扩展推断AI 解读

Stochastic approximation (SA) is a powerful and scalable computational method for iteratively estimating the solution of optimization problems in the presence of randomness, particularly well suited for large-scale and streaming data settings. In this work we propose a theoretical framework for stochastic approximation (SA) applied to nonparametric least squares in reproducing kernel Hilbert spaces (RKHS), enabling online statistical inference in nonparametric regression…

谱感知去偏：现代推断框架及其在主成分回归中的应用AI 解读

Debiasing is a fundamental concept in high-dimensional statistics. While degrees-of-freedom adjustment is the state-of-the-art debiasing technique in high-dimensional linear regression, it largely remains limited to independent, identically distributed samples and sub-Gaussian covariates. These limitations hinder its wider practical use. In this paper we break this barrier and introduce Spectrum-Aware Debiasing—a novel inference method that applies to challenging…