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面向个性化预测的可扩展协同排序AI 解读

Ben Daiab, Xiaotong Shena, Junhui Wangb & Annie Quc* a School of Statistics, University of Minnesota , Minneapolis , MN ; b School of Data Science, City University of Hong Kong , Kowloon Tong, Hong Kong ; c Department of Statistics, University of Illinois at Urbana Champaign , Champaign , IL Supplementary materials for this article are available online. Please go to www.tandfonline.com/r/JASA. CONTACT Annie Qu anniequ@illinois.edu Department of Statistics, University of…

具有结构突变的动态半参数因子模型AI 解读

For the change-point analysis of a high-dimensional time series, we consider a semiparametric model with dynamic structural break factors. With our model, the observations are described by a few low-dimensional factors with time-invariant loading functions of the covariates. Regarding the structural break, the factors are assumed to be nonstationary and follow a vector autoregression process with a change in the parameter values. In addition, to account for the known spatial…

具有非平稳性与部分可观测因子结构的变系数面板数据模型AI 解读

In this article, we study a varying-coefficient panel data model with both nonstationarity and partially observed factor structure. Two approaches are proposed. The first approach proposed in the main text considers a sieve based method to estimate the unknown coefficients as well as the factors and loading functions simultaneously, while the second approach proposed in the online supplementary document involving the principal component analysis provides an alternative…

具有条件稀疏结构的大型协方差矩阵的非参数估计AI 解读

This paper studies estimation of covariance matrices with conditional sparse structure. We overcome the challenge of estimating dense matrices using a factor structure, the challenge of estimating large-dimensional matrices by postulating sparsity on covariance of random noises, and the challenge of estimating varying matrices by allowing factor loadings to smoothly change. A kernel-weighted estimation approach combined with generalised shrinkage is proposed. Under some…

非可分面板模型的低秩近似AI 解读

Summary We provide estimation methods for nonseparable panel models based on low-rank factor structure approximations. The factor structures are estimated by matrix-completion methods to deal with the computational challenges of principal component analysis in the presence of missing data. We show that the resulting estimators are consistent in large panels, but suffer from approximation and shrinkage biases. We correct these biases using matching and…

利用横截面均值增广时间序列回归进行预测AI 解读

Summary There is a large and growing body of literature concerned with forecasting time series variables by the use of factor-augmented regression models. The workhorse of this literature is a two-step approach in which the factors are first estimated by applying the principal components method to a large panel of variables, and the forecast regression is then estimated, conditional on the first-step factor estimates. Another stream of research that has attracted much…

带交互固定效应的面板VAR模型AI 解读

Summary In the literature, a common feature of panel models with interactive fixed effects is that they model a univariate variable. In this regard, they are incapable of addressing dynamic and simultaneous interactions among a set of macroeconomic variables, a problem that falls within the realm of structural analysis. This paper aims to contribute to the existing literature by studying a homogeneous panel vector autoregression (VAR) model with interactive fixed effects.…

因子驱动的双区制回归AI 解读

We propose a novel two-regime regression model where regime switching is driven by a vector of possibly unobservable factors. When the factors are latent, we estimate them by the principal component analysis of a panel data set. We show that the optimization problem can be reformulated as mixed integer optimization, and we present two alternative computational algorithms. We derive the asymptotic distribution of the resulting estimator under the scheme that the threshold…

一般谱主体下线性混合模型的主成分AI 解读

We study the principal components of covariance estimators in multivariate mixed-effects linear models. We show that, in high dimensions, the principal eigenvalues and eigenvectors may exhibit bias and aliasing effects that are not present in low-dimensional settings. We derive the first-order limits of the principal eigenvalue locations and eigenvector projections in a high-dimensional asymptotic framework, allowing for general population spectral distributions for the…

理解动量与反转AI 解读

Stock momentum, long-term reversal, and other past return characteristics that predict future returns also predict future realized betas, suggesting these characteristics capture time-varying risk compensation. We formalize this argument with a conditional factor pricing model. Using instrumented principal components analysis, we estimate latent factors with time-varying factor loadings that depend on observable firm characteristics. We show that factor loadings vary…

差异潜变量图模型估计及其脑连接应用AI 解读

Summary Differential graphical models are designed to represent the difference between the conditional dependence structures of two groups, and thus are of particular interest for scientific investigations. Motivated by modern applications, this manuscript considers an extended setting where each group is generated by a latent variable Gaussian graphical model. Due to the existence of latent factors, the differential network is decomposed into sparse and low-rank components,…

随机缺失下的因子模型：EM 估计、推断与交叉验证AI 解读

We consider the estimation and inference in approximate factor models with random missing values. We show that with the low rank structure of the common component, we can estimate the factors and factor loadings consistently with the missing values replaced by zeros. We establish the asymptotic distributions of the resulting estimators and those based on the EM algorithm. We also propose a cross-validation-based method to determine the number of factors in factor models with…

统计因子模型下的高维最小方差组合估计AI 解读

We propose a high dimensional minimum variance portfolio estimator under statistical factor models, and show that our estimated portfolio enjoys sharp risk consistency. Our approach relies on properly integrating ℓ1 constraint on portfolio weights with an appropriate covariance matrix estimator. In terms of covariance matrix estimation, we extend the theoretical results of POET (Fan et al., 2013) to a setting that is coherent with principal component analysis. Simulation and…

时变广义动态因子模型与金融关联度测量AI 解读

We propose a new time-varying Generalized Dynamic Factor Model for high-dimensional, locally stationary time series. Estimation is based on dynamic principal component analysis jointly with singular VAR estimation, and extends to the locally stationary case the one-sided estimation method proposed by Forni et al. (2017) for stationary data. We prove consistency of our estimators of time-varying impulse response functions as both the sample size T and the dimension n of the…

半参数分位数因子模型的估计与推断AI 解读

We consider a semiparametric quantile factor panel model that allows observed stock-specific characteristics to affect stock returns in a nonlinear time-varying way, extending Connor, Hagmann, and Linton (2012) to the quantile restriction case. We propose a sieve-based estimation methodology that is easy to implement. We provide tools for inference that are robust to the existence of moments and to the form of weak cross-sectional dependence in the idiosyncratic error term.…

增广因子模型：市场风险因子验证与债券风险溢价预测AI 解读

We study factor models augmented by observed covariates that have explanatory powers on the unknown factors. In financial factor models, the unknown factors can be reasonably well explained by a few observable proxies, such as the Fama–French factors. In diffusion index forecasts, identified factors are strongly related to several directly measurable economic variables such as consumption-wealth variable, financial ratios, and term spread. With those covariates, both the…

误设模型的广义聚合：资产定价应用AI 解读

We propose a generalized aggregation approach for model averaging. The entropy-based optimal criterion is a natural choice for aggregating information from many “globally” misspecified models as it adapts better to the underlying model uncertainty and obtains more robust approximations. Unlike almost all other approaches in the existing literature, we do not require a “reference model,” or a true data generation process contained in the set of models — neither implicitly nor…

带正则化的最大线性回归模型AI 解读

Motivated by the newly developed max-linear competing copula factor models and max-stable nonlinear time series models, we propose a new class of max-linear regression models to take advantages of easy interpretable features embedded in linear regression models. It can be seen that linear relation is a special case of max-linear relation. We develop an EM algorithm based maximum likelihood estimation procedure. The consistency and asymptotics of the estimators for parameters…

用逐节点回归估计高维投资组合AI 解读

This article investigates the large sample properties of the variance, weights, and risk of high-dimensional portfolios where the inverse of the covariance matrix of excess asset returns is estimated using a technique called nodewise regression. Nodewise regression provides a direct estimator for the inverse covariance matrix using the least absolute shrinkage and selection operator to estimate the entries of a sparse precision matrix. We show that the variance, weights, and…

全球、地区特定与国家特定不确定性的时变影响AI 解读

We develop a dynamic factor model with time-varying parameters and stochastic volatility, estimate it using a large panel of macroeconomic and financial data for 22 countries and decompose the variance of each variable in terms of contributions from uncertainty common to all countries (“global uncertainty”), region-specific uncertainty, and country-specific uncertainty. Among other findings, the estimates suggest that global uncertainty plays a primary role in explaining the…

通过模型复杂度选择估计主流形AI 解读

Abstract We propose a framework of principal manifolds to model high-dimensional data. This framework is based on Sobolev spaces and designed to model data of any intrinsic dimension. It includes principal component analysis and principal curve algorithm as special cases. We propose a novel method for model complexity selection to avoid overfitting, eliminate the effects of outliers and improve the computation speed. Additionally, we propose a method for identifying the…

尖峰可分离协方差矩阵与主成分AI 解读

We study a class of separable sample covariance matrices of the form Q˜1:=A˜1/2XB˜X∗A˜1/2. Here, A˜ and B˜ are positive definite matrices whose spectrums consist of bulk spectrums plus several spikes, that is, larger eigenvalues that are separated from the bulks. Conceptually, we call Q˜1 a spiked separable covariance matrix model. On the one hand, this model includes the spiked covariance matrix as a special case with B˜=I. On the other hand, it allows for more general…

不均衡与不完整数据矩阵的子空间估计及误差保证AI 解读

This paper is concerned with estimating the column space of an unknown low-rank matrix A⋆∈Rd1×d2, given noisy and partial observations of its entries. There is no shortage of scenarios where the observations—while being too noisy to support faithful recovery of the entire matrix—still convey sufficient information to enable reliable estimation of the column space of interest. This is particularly evident and crucial for the highly unbalanced case where the column dimension…

近单位根交互效应面板中的因子分析方法AI 解读

In a recent study, Bai (2013) proposes a new factor analytical (FA) method for estimation of stationary dynamic panel data models with fixed effects. Our interest in this method originates with the fact it does not require explicit demeaning of the data, a practice that is known to cause problems of bias and low power in near unit root panels. The purpose is to study the properties of FA when applied to such panels when the common component admits to a interactive effects…