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隔夜 GARCH–Itô 波动率模型AI 解读

Various parametric volatility models for financial data have been developed to incorporate high-frequency realized volatilities and better capture market dynamics. However, because high-frequency trading data are not available during the close-to-open period, the volatility models often ignore volatility information over the close-to-open period and thus may suffer from loss of important information relevant to market dynamics. In this article, to account for whole-day…

递增凹与递增凸随机序约束下分布函数的一致估计AI 解读

A random variable Y1 is said to be smaller than Y2 in the increasing concave stochastic order if E[ϕ(Y1)]≤E[ϕ(Y2)] for all increasing concave functions ϕ for which the expected values exist, and smaller than Y2 in the increasing convex order if E[ψ(Y1)]≤E[ψ(Y2)] for all increasing convex ψ. This article develops nonparametric estimators for the conditional cumulative distribution functions Fx(y)=ℙ(Y≤y|X=x) of a response variable Y given a covariate X, solely under the…

可扩展的频率学派模型平均方法AI 解读

Frequentist model averaging is an effective technique to handle model uncertainty. However, calculation of the weights for averaging is extremely difficult, if not impossible, even when the dimension of the predictor vector, p, is moderate, because we may have 2p candidate models. The exponential size of the candidate model set makes it difficult to estimate all candidate models, and brings additional numeric errors when calculating the weights. This article proposes a…

多投入与多产出的非参数随机前沿模型AI 解读

Stochastic frontier models along the lines of Aigner et al. are widely used to benchmark firms’ performances in terms of efficiency. The models are typically fully parametric, with functional form specifications for the frontier as well as both the noise and the inefficiency processes. Studies such as Kumbhakar et al. have attempted to relax some of the restrictions in parametric models, but so far all such approaches are limited to a univariate response variable. Some…

存在大量最高效率企业时的随机前沿 LASSO 估计AI 解读

We apply the adaptive LASSO to select a set of maximally efficient firms in the panel fixed-effect stochastic frontier model. The adaptively weighted L1 penalty with sign restrictions allows simultaneous selection of a group of maximally efficient firms and estimation of firm-level inefficiency parameters with a faster rate of convergence than least squares dummy variable estimators. Our estimator possesses the oracle property. We propose a tuning parameter selection…

结合预检验与装袋法的投资组合选择AI 解读

This article exploits the idea of combining pretesting and bagging to choose between competing portfolio strategies. We propose an estimator for the portfolio weight vector, which optimally trades off Type I against Type II errors when choosing the best investment strategy. Furthermore, we accommodate the idea of bagging in the portfolio testing problem, which helps to avoid sharp thresholding and reduces turnover costs substantially. Our Bagged Pretested Portfolio Selection…

含离散与连续解释变量的回归模型设定检验AI 解读

This article proposes a nonparametric projection-based adaptive-to-model specification test for regressions with discrete and continuous predictors. The test statistic is asymptotically normal under the null hypothesis and omnibus against alternative hypotheses. The test behaves like a locally smoothing test as if the number of continuous predictors was one and can detect the local alternative hypotheses distinct from the null hypothesis at the rate that can be achieved by…

结合符号限制与外部工具变量识别 SVAR 模型AI 解读

We discuss combining sign restrictions with information in external instruments (proxy variables) to identify structural vector autoregressive (SVAR) models. In one setting, we assume the availability of valid external instruments. Sign restrictions may then be used to identify further orthogonal shocks, or as an additional piece of information to pin down the shocks identified by the external instruments more precisely. In a second setting, we assume that proxy variables…

高维成分数据的稳健协方差估计及销售数据应用AI 解读

Compositional data arises in a wide variety of research areas when some form of standardization and composition is necessary. Estimating covariance matrices is of fundamental importance for high-dimensional compositional data analysis. However, existing methods require the restrictive Gaussian or sub-Gaussian assumption, which may not hold in practice. We propose a robust composition adjusted thresholding covariance procedure based on Huber-type M-estimation to estimate the…

时变系数合成控制：结合贝叶斯收缩的状态空间方法AI 解读

Synthetic control methods are a popular tool for measuring the effects of policy interventions on a single treated unit. In practice, researchers create a counterfactual using a linear combination of untreated units that closely mimic the treated unit. Oftentimes, creating a synthetic control is not possible due to untreated units’ dynamic characteristics such as integrated processes or a time varying relationship. These are cases in which viewing the counterfactual…

非平稳光滑随机场期望欧拉示性数曲线的估计AI 解读

The expected Euler characteristic (EEC) of excursion sets of a smooth Gaussian-related random field over a compact manifold approximates the distribution of its supremum for high thresholds. Viewed as a function of the excursion threshold, the EEC of a Gaussian-related field is expressed by the Gaussian kinematic formula (GKF) as a finite sum of known functions multiplied by the Lipschitz–Killing curvatures (LKCs) of the generating Gaussian field. This paper proposes…

单位超球面上的空间分位数AI 解读

We propose a concept of quantiles for probability measures on the unit hypersphere Sd−1 of Rd. The innermost quantile is the Fréchet median, that is, the L1-analog of the Fréchet mean. The proposed quantiles μα,um are directional in nature: they are indexed by a scalar order α∈[0,1] and a unit vector u in the tangent space TmSd−1 to Sd−1 at m. To ensure computability in any dimension d, our quantiles are essentially obtained by considering the Euclidean (Chaudhuri (J. Amer.…

非参数条件局部独立性检验AI 解读

Conditional local independence is an asymmetric independence relation among continuous time stochastic processes. It describes whether the evolution of one process is directly influenced by another process given the histories of additional processes, and it is important for the description and learning of causal relations among processes. We develop a model-free framework for testing the hypothesis that a counting process is conditionally locally independent of another…

稳健高维免调参多重检验AI 解读

A stylized feature of high-dimensional data is that many variables have heavy tails, and robust statistical inference is critical for valid large-scale statistical inference. Yet, the existing developments such as Winsorization, Huberization and median of means require the bounded second moments and involve variable-dependent tuning parameters, which hamper their fidelity in applications to large-scale problems. To liberate these constraints, this paper revisits the…

We propose a general optimization-based framework for computing differentially private M-estimators and a new method for constructing differentially private confidence regions. First, we show that robust statistics can be used in conjunction with noisy gradient descent or noisy Newton methods in order to obtain optimal private estimators with global linear or quadratic convergence, respectively. We establish local and global convergence guarantees, under both local strong…

通过逐次投影为文档分配主题AI 解读

Topic models provide a useful tool to organize and understand the structure of large corpora of text documents, in particular, to discover hidden thematic structure. Clustering documents from big unstructured corpora into topics is an important task in various fields, such as image analysis, e-commerce, social networks and population genetics. Since the number of topics is typically substantially smaller than the size of the corpus and of the dictionary, the methods of topic…

多个预测变量与生存结局最大关联的有效估计AI 解读

This paper develops a new approach to post-selection inference for screening high-dimensional predictors of survival outcomes. Post-selection inference for right-censored outcome data has been investigated in the literature, but much remains to be done to make the methods both reliable and computationally-scalable in high dimensions. Machine learning tools are commonly used to provide predictions of survival outcomes, but the estimated effect of a selected predictor suffers…

利用高频数据估计混合分数稳定过程AI 解读

The linear fractional stable motion generalizes two prominent classes of stochastic processes, namely stable Lévy processes, and fractional Brownian motion. For this reason, it may be regarded as a basic building block for continuous time models. We study a stylized model consisting of a superposition of independent linear fractional stable motions and our focus is on parameter estimation of the model. Applying an estimating equations approach, we construct estimators for…

未观测协变量对协变量自适应随机实验的影响AI 解读

Covariate-adaptive randomization (CAR) is commonly implemented in clinical trials to balance observed covariates. Recent studies have demonstrated the advantages of CAR procedures in balancing covariates and improving the subsequent statistical analysis. Covariate balance is crucial, but it is not a panacea for the valid statistical inferences. If the response to a treatment interacts with some unobserved covariates, the conclusion drawn from a CAR experiment may be…

一般高斯设计下的 Lasso 及其假设检验应用AI 解读

The Lasso is a method for high-dimensional regression, which is now commonly used when the number of covariates p is of the same order or larger than the number of observations n. Classical asymptotic normality theory does not apply to this model due to two fundamental reasons: (1) The regularized risk is nonsmooth; (2) The distance between the estimator θˆ and the true parameters vector θ∗ cannot be neglected. As a consequence, standard perturbative arguments that are the…

完全随机缺失的最优非参数检验及其与相容性的联系AI 解读

Given a set of incomplete observations, we study the nonparametric problem of testing whether data are Missing Completely At Random (MCAR). Our first contribution is to characterise precisely the set of alternatives that can be distinguished from the MCAR null hypothesis. This reveals interesting and novel links to the theory of Fréchet classes (in particular, compatible distributions) and linear programming, that allow us to propose MCAR tests that are consistent against…

依赖重尾数据的极端回归推断AI 解读

Nonparametric inference on tail conditional quantiles and their least squares analogs, expectiles, remains limited to i.i.d. data. We develop a fully operational inferential theory for extreme conditional quantiles and expectiles in the challenging framework of α-mixing, conditional heavy-tailed data whose tail index may vary with covariate values. This requires a dedicated treatment to deal with data sparsity in the far tail of the response, in addition to handling…

处理决策存在策略互动的处理效应模型AI 解读

This study considers treatment effect models in which others’ treatment decisions can affect both one’s own treatment and outcome. Focusing on the case of two-player interactions, we formulate treatment decision behavior as a complete information game with multiple equilibria. Using a latent index framework and assuming a stochastic equilibrium selection, we prove that the marginal treatment effect from one’s own treatment and that from the partner are identifiable on the…

简单合约的结构分析AI 解读

This paper provides an econometric framework for analyzing two-period simple contracts where an agent chooses between a fixed-price option and a cost-reimbursement option provided by a principal in each contracting period. First, we propose a consistent procedure for testing the null hypothesis of a corresponding cost function being linear, which is widely assumed for tractability in the literature. Motivated by the rejection of such a null based on our empirical data, next…