论文检索

动态状态空间 HAR 模型AI 解读

The Heterogeneous AutoRegressive model for the logs of Realised Volatility (HARL) has established itself as the benchmark specification for modelling and forecasting return volatility, owing to its parsimony and ability to capture the strong persistence typically observed in RV. To address potential concerns such as measurement errors, nonlinearities, and non-spherical residuals, numerous variants of the baseline HARL model have been developed in the literature. This paper…

区分暂时与持续成本无效率的银行业务模式贝叶斯非参数推断AI 解读

This paper introduces a novel econometric framework for identifying and modeling bank business models (BBMs), which dynamically evolve in response to changing financial and economic conditions. Building on the stochastic frontier literature, we extend the traditional cost-efficiency models by decomposing inefficiency into persistent and transient components. We propose a Bayesian nonparametric approach that adapts to the data through an infinite mixture model with…

非中心化随机波动率下结构向量自回归的部分识别AI 解读

We consider structural vector autoregressions that are identified through stochastic volatility under Bayesian estimation. Three contributions emerge from our exercise. First, we show that a non-centred parameterization of stochastic volatility yields a marginal prior for the conditional variances of structural shocks that is centred on homoskedasticity, with strong shrinkage and heavy tails—unlike the common centred parameterization. This feature makes it well suited for…

非随机缺失数据下的共同基金绩效贝叶斯评估：中国市场应用AI 解读

This paper studies mutual fund performance evaluation, where non-random missing data is an important issue. Ignoring sample selection issues can lead to biased parameter estimates and invalid performance evaluation. Therefore, we propose a novel Beta selection model to characterize this non-random missingness mechanism. We can significantly reduce the bias in alpha estimates, by introducing our Beta selection mechanism into the Noise-Reduced Alpha (NRA) model of Harvey and…

Copula 密度的半参数贝叶斯估计AI 解读

We propose a semiparametric Bayesian estimator for copula density, formulated as a generalized exponential family that combines a parametric baseline copula with a flexible adjustment modeled by a Gaussian Process (GP). The baseline captures the main features of dependence and accommodates possibly unbounded densities, while the GP component introduces nonparametric refinement. To modulate the influence of the baseline, we further introduce a tuning parameter that adjusts…

含持续与暂时无效率的半参数随机前沿模型贝叶斯估计AI 解读

We propose kernel estimators for the densities of persistent and transient inefficiencies in a generalized panel data stochastic frontier model. This approach relaxes the conventional distributional assumptions imposed on the inefficiencies, thereby increasing robustness. A Bayesian sampling algorithm is developed to estimate all model parameters and unobserved entities, as well as the bandwidths needed to construct the densities of persistent and transient inefficiencies.…

疫情后大型贝叶斯 VAR 的贝叶斯模型比较AI 解读

There is an increasing interest in applying variational Bayes techniques to estimating large Bayesian vector autoregressions (VARs) with stochastic volatility. However, less attention has been paid to the development of appropriate tools for comparing these high-dimensional models, especially among those designed to address COVID-19 outliers. This paper develops a marginal likelihood estimator that combines importance sampling and variational approximation for comparing…

气候计量中的贝叶斯时空变系数建模：时空高斯过程方法AI 解读

Quantifying the economic impacts of weather variables has been a key empirical topic in understanding the impacts of climate change. While panel methods, which utilize cross-sectional differences in within-unit over-time changes in weather variables, have been widely used to credibly identify the average effect of interest, there has been a growing interest in understanding the distribution of potentially heterogeneous weather effects. The heterogeneity of the effects across…

经济增长建模的贝叶斯方法：变量选择与截面依赖AI 解读

This paper introduces a Bayesian framework designed to address two critical econometric challenges in empirical growth studies: variable selection and cross-sectional dependence. We first propose a growth regression model that incorporates cross-sectional dependence through a multi-factor structure and accounts for variable selection with sparse coefficients. We then introduce a new Bayesian Adaptive LASSO estimator specifically designed for this model, capable of performing…

贝叶斯模型选择中偏差信息准则的理论依据与应用AI 解读

This paper provides a theoretical justification for the Deviance Information Criterion (DIC) as a Bayesian model selection tool using MCMC output. Unlike Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), which balance model adequacy against complexity without considering prior information, DIC incorporates priors into this trade-off. The contributions of this paper are two-fold. First, it demonstrates that when a plug-in predictive distribution —…

序数 Probit 模型的半参数贝叶斯估计：跨国、年龄与性别的生活满意度AI 解读

We employ a novel semiparametric Bayesian ordinal probit model to re-examine the relationships between age and life satisfaction (“happiness”) across countries and gender. Within our ordinal choice framework we introduce a new scheme for cutpoint simulation and develop a locally adaptive method for smoothing an otherwise erratic collection of age dummy variable coefficients. We find strong evidence that employment status is deeply intertwined with the reported happiness of…

大型贝叶斯矩阵自回归模型AI 解读

High-dimensional matrix-valued time-series are increasingly common in economics and finance. Prominent examples include large cross-region panels and dynamic economic networks. As the dimensions of the matrix grow, conventional approaches based on vector autoregressions—implemented by vectoring the matrix-valued data—become computationally infeasible. We introduce a class of large Bayesian matrix autoregressions (BMARs) that can accommodate time-varying volatility,…

金融素养与金融犯罪：回归不连续设计AI 解读

This study investigates how financial literacy shapes the propensity of individuals to commit financial crime. Using state-level administrative data on criminal charges linked to comprehensive public records , we exploit a policy-based discontinuity in grade level assignment based on individual birth dates that exogenously requires certain high school cohorts to attend a financial literacy course. Our estimates suggest that exposure to the course reduces the propensity to…

玻璃盒机器学习与公司债券收益AI 解读

Machine learning methods in asset pricing are often criticized for their black box nature. We study this issue by predicting corporate bond returns using interpretable machine learning on a high-dimensional bond characteristics data set. We achieve state-of-the-art performance while maintaining an interpretable model structure, overcoming the accuracy–interpretability trade-off. The estimation uncovers nonlinear relationships and economically meaningful interactions in bond…

校正测量偏差：大型教育评估中国家排名的新框架AI 解读

International Large-scale Assessments (ILSAs), such as the Program for International Student Assessment (PISA) and the Trends in International Mathematics and Science Study (TIMSS), are cornerstone tools for global educational research and policy-making. By benchmarking educational quality and performance trends, these assessments enable countries to evaluate and share effective pedagogical structures. Specifically, ILSAs employ Item Response Theory (IRT) models to rank…

Edgeworth 隐私核算器：差分隐私组合的解析方法AI 解读

In privacy-preserving data analysis, many procedures and algorithms are structured as compositions of multiple private building blocks. As such, an important question is how to efficiently compute the overall privacy loss under composition. This paper introduces the Edgeworth Accountant, an analytical approach to composing differential privacy guarantees for private algorithms. Leveraging the f-differential privacy framework (Dong et al., 2022), the Edgeworth Accountant…

高斯混合模型的稳健无监督多任务与迁移学习AI 解读

Unsupervised learning has been widely used in many real-world applications. One of the simplest and most important unsupervised learning models is the Gaussian mixture model (GMM). In this work, we study the multi-task learning problem on GMMs, which aims to leverage potentially similar GMM parameter structures among tasks to obtain improved learning performance compared to single-task learning. We propose a multi-task GMM learning procedure based on the EM algorithm that…

高维局部依赖数据的广义隶属度估计AI 解读

This work focuses on the mixed membership models for multivariate categorical data widely used for analyzing survey responses and population genetics data. These grade of membership (GoM) models offer rich modeling power but present significant estimation challenges for high-dimensional polytomous data. Popular existing approaches, such as Bayesian MCMC inference, are not scalable and lack theoretical guarantees in high-dimensional settings. To address this, we first observe…

估计分位数最优处理规则的序贯分类学习AI 解读

Quantile optimal treatment regimes (OTRs) aim to assign treatments that maximize a specified quantile of patients’ outcomes. Compared to treatment regimes that target the mean outcomes, quantile OTRs offer fairer regimes when a lower quantile is selected, as it improves outcomes for vulnerable patients. In this paper, we propose a novel method for estimating quantile OTRs by reformulating the problem as a successive classification task, solvable via training a sequence of…

用于域泛化的域特定非参数回归AI 解读

We propose a domain-specific regression approach for domain generalization, taking into account possible heterogeneity among the datasets from different sources. In the proposed model, the domain-specific features are characterized through linear functionals of the marginal source distributions. The predictors are combined with the domain-specific linear functionals as inputs in the model. Using the source data sets, we estimate the domain-index function and the regression…

从树集成中提取可解释模型：计算与统计视角AI 解读

Tree ensembles are non-parametric methods widely recognized for their accuracy and ability to capture complex interactions. While these models excel at prediction, they are difficult to interpret and may fail to uncover useful relationships in the data. We propose an estimator to extract compact sets of decision rules from tree ensembles. The extracted models are accurate and can be manually examined to reveal relationships between the predictors and the response. A key…

存在隐藏混杂时过参数化模型的在线推断AI 解读

Abstract In this paper, we study online estimation and inference of regression coefficients in the presence of hidden confounders by leveraging over-parameterized models. Unlike existing offline approaches that rely on factor and sparse models, our closed-form estimator simultaneously removes hidden-confounder bias and is directly applicable to streaming data. Using tools from random matrix theory, we analyse phase transition phenomena in the variance of the coefficient…

混杂变量非可忽略缺失下中介分析的有效非参数推断AI 解读

Mediation analysis is widely used for exploring treatment mechanisms; however, it faces challenges when nonignorable missing confounders are present. Efficient inference of mediation effects and the efficiency loss due to nonignorable missingness have been rarely studied in the literature because of the difficulties arising from the ill-posed inverse problem. In this paper, we propose a general shadow variable framework for identifying mediation effects, allowing shadow…

时空极端事件的模式AI 解读

In environmental science applications, extreme events frequently exhibit a complex spatio-temporal structure, which is difficult to describe flexibly and estimate in a computationally efficient way using state-of-art parametric extreme-value models. In this paper, we propose a computationally-cheap non-parametric approach to investigate the probability distribution of temporal clusters of spatial extremes, and study within-cluster patterns with respect to various…