论文检索

局部隐私估计中的适当复杂度度量：超越 Fisher 信息AI 解读

We identify fundamental tradeoffs between statistical utility and privacy under local models of privacy in which data is kept private even from the statistician, providing instance-specific bounds for private estimation and learning problems by developing the local minimax risk. In contrast to approaches based on worst-case (minimax) error, which are conservative, this allows us to evaluate the difficulty of individual problem instances and delineate the possibilities for…

重印：高维分位数回归的假设检验AI 解读

Quantile regression has been an important analytical tool in econometrics since it was proposed in 1970s. Many advantages make it still popular in the era of big data. This paper focuses on the testing problems of high-dimensional quantile regression, supplementing to robust methods in the literature of high-dimensional hypothesis testing. We first construct a new test statistic based on the quantile regression score function. The new test statistic avoids the inverse…

重印：高维线性中介模型推断及新冠疫情股票反应应用AI 解读

Mediation analysis draws increasing attention in many research areas such as economics, finance and social sciences. In this paper, we propose new statistical inference procedures for high dimensional mediation models, in which both the outcome model and the mediator model are linear with high dimensional mediators. Traditional procedures for mediation analysis cannot be used to make statistical inference for high dimensional linear mediation models due to…

利用稳健波动率代理比较波动率预测：经验偏差视角AI 解读

Volatility forecasting is crucial to risk management and portfolio construction. One particular challenge of assessing volatility forecasts is how to construct a robust proxy for the unknown true volatility. In this work, we show that the empirical loss comparison between two volatility predictors hinges on the deviation of the volatility proxy from the true volatility. We then establish non-asymptotic deviation bounds for three robust volatility proxies, two of which are…

双模网络的二部网络影响分析AI 解读

A two-mode network contains two types of nodes, and edges exist only between any two nodes that are associated with different entities. Owing to the network connections (i.e., edges) between the two types of network nodes, nodal responses are unlikely to be independently and identically distributed, resulting in possible nodal heterogeneity across the two types of nodes. This study proposes a novel bipartite network influence model (BNIM) to evaluate nodal heterogeneity from…

大量协变量下最优政策效应的推断AI 解读

Understanding the impact of the most effective policies or treatments on a response variable of interest is desirable in many empirical works in economics, statistics and other disciplines. Due to the widespread winner’s curse phenomenon, conventional statistical inference assuming that the top policies are chosen independent of the random sample may lead to overly optimistic evaluations of the best policies. In recent years, given the increased availability of large…

Retire：高维稳健期望分位数回归AI 解读

High-dimensional data can often display heterogeneity due to heteroscedastic variance or inhomogeneous covariate effects. Penalized quantile and expectile regression methods offer useful tools to detect heteroscedasticity in high-dimensional data. The former is computationally challenging due to the non-smooth nature of the check loss, and the latter is sensitive to heavy-tailed error distributions. In this paper, we propose and study (penalized) robust expectile regression…

利用 Fisher 方法增强多因子资产定价模型检验功效AI 解读

Testing multi-factor asset pricing models is instrumental for asset pricing theory and practice. However, due to the accumulation of errors in estimating high-dimensional parameters, traditional quadratic-form tests such as the Wald test perform poorly against the sparse alternative hypothesis, i.e., a few mispriced assets. Fan et al. (2015b) introduced a powerful testing procedure by adding a power enhancement component to the Wald test statistic and proved power…

异步、含噪与高维高频数据的已实现回归AI 解读

We develop regression for high frequency data. This regression is novel in that it can be for both fixed and increasing dimension. Also, the data may have microstructure noise, and observations (trades, or quotes) can be asynchronous, (i.e., the observations do not need to be synchronized across dimensions). As is customary for high-frequency inference methods, we refer to our method as “realized” regression.

广义动态因子模型的推断理论AI 解读

We provide the asymptotic distributional theory for the so-called General or Generalized Dynamic Factor Model (GDFM), laying the foundations for an inferential approach in the GDFM analysis of high-dimensional time series. By exploiting the duality between common shocks and dynamic loadings, we derive the asymptotic distribution and associated standard errors for a class of estimators for common shocks, dynamic loadings, common components, and impulse response functions. We…

挖掘因子动物园：借助充分代理变量估计潜在因子模型AI 解读

Latent factor model estimation typically relies on either using domain knowledge to manually pick several observed covariates as factor proxies, or purely conducting multivariate analysis such as principal component analysis. However, the former approach may suffer from the bias while the latter cannot incorporate additional information. We propose to bridge these two approaches while allowing the number of factor proxies to diverge, and hence make the latent factor model…

Markowitz 投资组合的稳健化AI 解读

Markowitz mean–variance portfolios with sample mean and covariance as input parameters feature numerous issues in practice. They perform poorly out of sample due to estimation error, they experience extreme weights together with high sensitivity to change in input parameters. The heavy-tail characteristics of financial time series are in fact the cause for these erratic fluctuations of weights that consequently create substantial transaction costs. In robustifying the…

超高维数据筛选后的诊断检验AI 解读

We propose a consistent lack-of-fit test to assess whether replacing the original ultrahigh dimensional covariates with a given number of linear combinations results in a loss of regression information. To attenuate the spurious correlations that may inflate type-I error rates in high dimensions, we suggest to randomly split the observations into two parts. In the first part, we screen out as many irrelevant covariates as possible. This screening step helps to reduce the…

时变最小方差投资组合AI 解读

This paper proposes a new time-varying minimum variance portfolio (TV-MVP) in a large investment universe of assets. Our method extends the existing literature on minimum variance portfolios by allowing for time-varying factor loadings, which facilitates the capture of the dynamics of the covariance structure of asset returns (and hence, the optimal investment strategy in a dynamic setting). We also use a shrinkage estimation method based on a quasi-likelihood function to…

结构变化检测中控制错误发现率的广义Knockoff程序AI 解读

Controlling false discovery rate (FDR) is crucial for variable selection, multiple testing, among other signal detection problems. In literature, there is certainly no shortage of FDR control strategies when selecting individual features, but the relevant works for structural change detection, such as profile analysis for piecewise constant coefficients and integration analysis with multiple data sources, are limited. In this paper, we propose a generalized knockoff…

高频数据中高维噪声协方差矩阵的最优估计AI 解读

We consider high-dimensional measurement errors with high-frequency data. Our objective is on recovering the high-dimensional cross-sectional covariance matrix of the random errors with optimality. In this problem, not all components of the random vector are observed at the same time and the measurement errors are latent variables, leading to major challenges besides high data dimensionality. We propose a new covariance matrix estimator in this context with appropriate…

具有共同结构的面板多折点分位数回归模型AI 解读

Stimulated by the analysis of a data set on financial portfolio returns, we propose a multi-kink quantile regression (MKQR) model with latent homogeneous structure for panel data analysis. The proposed model accounts for both homogeneity and heterogeneity among individuals and parameters in panel data analysis. From statistical modeling point of view, it well balances the risk of misspecification and the model parsimony. From practical point of view, it is able to reveal not…

可分度量空间中多个分布的相等性检验：最大均值差异方法AI 解读

A new test for equal distributions of several high-dimensional samples in separable metric spaces, with its test statistic constructed based on maximum mean discrepancy, is proposed and studied. The asymptotic null and alternative distributions of the test statistic are established under some mild conditions. The new test is implemented via a three-cumulant matched chi-square approximation with the associated approximation parameters consistently estimated from the data. A…

随机前沿分析中无效率分布设定的检验AI 解读

Stochastic frontier analysis is regularly used in empirical studies to evaluate the productivity and efficiency of companies. A typical stochastic frontier model involves a parametric frontier subject to a composite error term consisting of an inefficiency and a random error. We develop new tests for specification of distribution of the inefficiency. We focus on simultaneous relaxation of two common assumptions: (1) parametric frontier which may lead to false conclusions…

异常值引起的回归系数扭曲检验及气候变化应用AI 解读

Outlying observations can bias regression estimates, requiring the use of outlier-robust estimators. Comparing robust estimates to those obtained using ordinary least squares (OLS) is a common robustness check, however, such comparisons have been mostly informal due to the lack of available tests. Here we introduce a formal test for coefficient distortion due to outliers in regression models. Our proposed test is based on the difference between OLS and robust estimates…

大型时空自回归的稀疏广义 Yule–Walker 估计：卫星二氧化氮数据应用AI 解读

We consider a high-dimensional model in which variables are observed over time and space. The model consists of a spatio-temporal regression containing a time lag and a spatial lag of the dependent variable. Unlike classical spatial autoregressive models, we do not rely on a predetermined spatial interaction matrix, but infer all spatial interactions from the data. Assuming sparsity, we estimate the spatial and temporal dependence fully data-driven by penalizing a set of…

圆周时间序列建模AI 解读

Circular variables often play an important role in the construction of models for analysing and forecasting the consequences of climate change and its impact on the environment. Such variables pose special problems for time series modelling. This article shows how the score-driven approach, developed primarily in econometrics, provides a natural solution to the difficulties and leads to a coherent and unified methodology for estimation, model selection and testing. The new…

阈值自回归 bootstrap 检验的有效性AI 解读

We consider bootstrap-based testing for threshold effects in non-linear threshold autoregressive (TAR) models. It is well-known that classic tests based on asymptotic theory tend to be biased in case of small, or even moderate sample sizes, especially when the estimated parameters indicate non-stationarity, or in presence of heteroskedasticity, as often witnessed in the analysis of financial or climate data. To address the issue we propose a supremum Lagrange Multiplier test…

线性时变系数模型的筛自助法推断AI 解读

We propose a sieve bootstrap framework to conduct pointwise and simultaneous inference for time-varying coefficient regression models based on a local linear estimator. The asymptotic validity of the sieve bootstrap in the presence of autocorrelation is established. The bootstrap automatically produces a consistent estimate of nuisance parameters, both at the interior and boundary points. In addition, we develop a bootstrap-based test for parameter constancy and examine its…