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面向重尾数据的高维切片逆 Kendall 秩相关估计AI 解读

Sufficient dimension reduction is an important branch of statistical learning, which performs an optimal projection of covariates under a supervised learning framework. Most of the existing sufficient dimension reduction algorithms require strict restrictions on the distribution of models, making them sensitive to heavy-tailed predictors and outliers. In this article, we propose a novel method called Sliced Inverse Kendall’s tau Estimation (SIKE) designed for analyzing…

大冲击宏观模型的快速高效估计AI 解读

We propose a new approach for efficiently estimating and analyzing macroeconomic models subject to large shocks. We apply the methodology to a two-sector model that captures the heterogeneous exposure of different sectors to the COVID-19 pandemic. We solve the model nonlinearly and propose a new nonlinear, non-Gaussian filter designed to handle large shocks and identify their source and time location. Monte Carlo experiments show that the estimation and identification of…

矩阵分位数因子模型AI 解读

This paper introduces a matrix quantile factor model for matrix-valued data with low-rank structure. We estimate the row and column factor spaces via minimizing the empirical check loss function with orthogonal rotation constraints. We show that the estimates converge at rate (min{p1p2,p2T,p1T})−1/2 in the average Frobenius norm, where p1, p2 and T are the row dimensionality, column dimensionality and length of the matrix sequence, respectively. This rate is faster than that…

高维干扰参数下分位数回归高维效应的去相关检验AI 解读

Quantile regression provides a flexible framework in econometrics for modeling the relationship between a response variable and multivariate predictors, particularly when heterogeneous effects are present. This paper addresses the challenge of testing high-dimensional coefficients in quantile regression in the presence of high-dimensional nuisance parameters. We first extend a recently proposed score test by incorporating a convolution-type smoothed quantile loss, which…

一族相干风险度量：在险价值的无限加权平均AI 解读

Value-at-Risk (VaR) remains one of the most widely used risk measures in finance due to its simplicity, interpretability, and regulatory prominence, particularly within the Basel framework. However, VaR is not a coherent risk measure, as it generally fails to satisfy the subadditivity property. This paper demonstrates that, although finite weighted combinations of VaR-type functionals do not necessarily yield coherent risk measures, appropriately constructed infinite…

利用大时间维度面板数据估计未观测的横截面网络连接AI 解读

We propose a simple three-step method to estimate the cross-sectional interdependence in a customary network econometric model with an unobserved network and a large T. First, we estimate the reduced-form model to construct instruments for the endogenous peer outcomes. Second, we use two-stage least squares to estimate the direct and contextual effects of the exogenous regressors, as well as the endogenous peer effect. Third, we estimate the network weight matrix. Monte…

面板MIDAS模型非线性最小二乘估计量的渐近分布AI 解读

The Mixed Data Sampling (MIDAS) approach provides a flexible framework for incorporating mixed-frequency data into econometric models. In this paper, we introduce the MIDAS framework into a panel data model and derive the asymptotic properties of the NLS estimator under a joint asymptotic framework where both N and T tend to infinity, allowing for heteroskedasticity and arbitrary correlation within each individual. To facilitate valid inference, we construct robust…

从旋转不变性到标量不变性：提升得分驱动因子模型的可识别性AI 解读

We show that, for a certain class of scaling matrices including the inverse square-root of the conditional Fisher information, score-driven factor models are identifiable up to a multiplicative scalar constant under very mild restrictions. This result has no analogue in parameter-driven models, as it exploits the different structure of the score-driven factor dynamics. Consequently, score-driven factor models overcome the issue of rotational invariance that typically affects…

用非线性因子扩展贝叶斯 VAR 的灵活方法AI 解读

This paper proposes a vector autoregression augmented with nonlinear factors that are modeled nonparametrically using regression trees. There are four main advantages of our model. First, modeling potential nonlinearities nonparametrically lessens the risk of misspecification. Second, the use of factor methods ensures that departures from linearity are modeled parsimoniously. In particular, they exhibit functional pooling where a small number of nonlinear factors are used to…

存在少报的有序选择模型中的变化中的变化AI 解读

We develop a Difference-in-Differences framework for discrete, ordered outcomes subject to underreporting. Such outcomes commonly arise in self-reported surveys on socially undesirable or stigmatized behaviors, where respondents may conceal their true behavior. For a discrete Changes-in-Changes model that is shown to admit an equivalent threshold-crossing representation, we derive nonparametric bounds for the counterfactual and factual outcome distributions as well as for…

函数型时间序列的近似因子模型AI 解读

We propose a novel approximate factor model tailored for analyzing time-dependent curve data. Our model decomposes such data into two distinct components: a low-dimensional predictable factor component and an unpredictable error term. These components are identified through the autocovariance structure of the underlying functional time series. The model parameters are consistently estimated using the eigencomponents of a cumulative autocovariance operator and an information…

线性因子模型的 Alpha 检验：投资组合方法AI 解读

This paper develops a test for evaluating linear factor models in high-dimensional settings. The proposed Maximum Random Sparse Portfolio (MRSP) test generates L random sparse portfolios, each obtained through K random draws, and then selects the hardest-to-price portfolio to assess factor model performance. By evaluating multiple randomly generated sparse portfolios, the MRSP test retains a portfolio-based economic interpretation while avoiding reliance on a single…

基于正态混合准极大似然的双重自回归模型稳健估计AI 解读

This paper investigates the estimation of the double autoregressive (DAR) model in the presence of skewed and heavy-tailed innovations. We propose a novel Normal Mixture Quasi-Maximum Likelihood Estimation (NM-QMLE) method to address the limitations of conventional quasi-maximum likelihood estimation (QMLE) under non-Gaussian conditions. By incorporating a normal mixture distribution into the quasi-likelihood framework, NM-QMLE effectively captures both heavy-tailed behavior…

多重门槛回归的 Lasso 后推断及收益可预测性应用AI 解读

This article revisits the multiple threshold regression model, wherein coefficient parameters shift across regimes in accordance with the value of a threshold variable. We focus on developing a post-Lasso inference framework to resolve the selection uncertainty—particularly pronounced when threshold effects are weak—intrinsic to the group Lasso-based procedure that jointly estimates the number and locations of multiple thresholds. This objective is accomplished by…

空间动态面板的再中心化矩估计量AI 解读

This paper proposes a recentered method of moments (RMM) approach for estimating spatial dynamic panel data (SDPD) models with both individual and time fixed effects. This approach directly exploits the correlation of regressors and disturbances, where heteroskedasticity may exist across units and/or over time. It is computationally tractable and can handle situations where other methods are not applicable. We show that the proposed estimator is consistent and asymptotically…

用户指定异方差参数模型下的回归有效估计AI 解读

We show that it is possible to estimate regression coefficients at least as precisely as Ordinary Least Squares (OLS), Weighted Least Squares (WLS) and its recently proposed refinements, regardless of the user’s parametric model for heteroskedasticity. The key is to modify WLS depending on scalar target parameters, e.g., coefficients, marginal effects, predictions, etc. to obtain a targeted WLS that maximizes precision of estimation of the target given the user’s model for…

高维异质数据的无模型分布稳健特征筛选与错误发现控制AI 解读

In this paper, we propose a model-free feature screening framework tailored for high-dimensional and heterogeneous datasets, based on a novel distributionally robust dependence measure termed Copula Divergence. The proposed screening method, named CD-Screen, addresses critical limitations of existing feature screening methods, such as restrictive modeling assumptions and sensitivity to heterogeneous feature distributions. CD-Screen ranks features according to their Copula…

STRAP：利用随机投影的多元同质性平滑检验AI 解读

In this paper, we propose STRAP (Smooth Tests using RAdom Projections), projected smooth tests for the equality of two multivariate distributions. We employ random projections to map the multivariate data into one-dimensional data and transform the projected data via the probability integral transformation. Under the null hypothesis of homogeneity, the transformed data follow the standard uniform distribution, reducing the problem to testing for uniformity. Building on…

设定 LASSO 与灵活的特征型资产定价模型AI 解读

This paper studies a partially linear semiparametric additive model that allows for interactions among variables in high-dimensional settings. We propose the Specification LASSO (S-LASSO), a two-step method that integrates LASSO and Adaptive Group LASSO to simultaneously perform variable selection and model specification. Theoretically, we establish that S-LASSO possesses oracle properties in terms of selection consistency and asymptotic normality. Monte Carlo simulations…

用多元分数布朗运动建模与预测已实现波动率AI 解读

A multivariate fractional Brownian motion (mfBm) with component-wise Hurst exponents is used to model and forecast realized volatility (RV). We investigate the interplay between correlation coefficients and Hurst exponents and propose a novel method to estimate model parameters, establishing its consistency and asymptotic normality. Additionally, we develop a time-reversibility test, which is typically not rejected by RV data. When the data generating process is a…

时间序列偏度、峰度与正态性的自归一化检验AI 解读

Testing for skewness, kurtosis, and normality for time series data is highly relevant for modeling and testing purposes in econometrics and statistics. It also affects our understanding of many economic and financial phenomena and the validity of the models developed to explain them. In this paper, we propose self-normalized tests for skewness, kurtosis, and normality that can eliminate the effect of the long-run variance (LRV). In particular, our tests allow us to avoid…

多矩阵自回归模型及其多模态网络应用AI 解读

Matrix-valued time-series data have become increasingly prevalent across diverse fields, including economics, finance, computer science, engineering, and signal processing. However, mixed matrix-vector structures in real-world data present challenges for existing methods. To fill in this gap, we propose a multi-matrix autoregressive (MMAR) model designed to jointly model matrix time series with varying structures. The key idea is to treat matrices as elements and to model…

近历元依赖重尾时间序列的极端局部线性分位数回归AI 解读

This paper develops a data-driven inference procedure for extreme analysis of data with near epoch dependence (NED), a condition less restrictive than traditional dependence structures like α-mixing, making it particularly useful for analyzing heavy-tailed time series data. To capture nonlinear data structures, we propose a new framework that combines local linear quantile regression with extreme value theory. We first study the asymptotic properties of the local-linear…

动态网络形成的非参数推断：对“动态网络形成中的同质性与传递性”的评论AI 解读

1 IntroductionDano et al. (2026) develop a novel conditional logit approach to identify the structural parameters of a model of dynamic network formation. They propose a conditional maximum likelih...