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最优数据披露风险评估AI 解读

Protection against disclosure is a legal and ethical obligation for agencies releasing microdata files for public use. Consider a microdata sample of size n from a finite population of size n¯=n+λn, with λ>0, such that each sample record contains two disjoint types of information: identifying categorical information and sensitive information. Any decision about releasing data is supported by the estimation of measures of disclosure risk, which are defined as discrete…

利用对称性与形状约束的高维非参数密度估计AI 解读

We tackle the problem of high-dimensional nonparametric density estimation by taking the class of log-concave densities on Rp and incorporating within it symmetry assumptions, which facilitate scalable estimation algorithms and can mitigate the curse of dimensionality. Our main symmetry assumption is that the super-level sets of the density are K-homothetic (i.e., scalar multiples of a convex body K⊆Rp). When K is known, we prove that the K-homothetic log-concave maximum…

多维分布函数、分位数函数、秩与符号：测度运输方法AI 解读

Unlike the real line, the real space Rd, for d≥2, is not canonically ordered. As a consequence, such fundamental univariate concepts as quantile and distribution functions and their empirical counterparts, involving ranks and signs, do not canonically extend to the multivariate context. Palliating that lack of a canonical ordering has been an open problem for more than half a century, generating an abundant literature and motivating, among others, the development of…

时间一致、非参数、非渐近的置信序列AI 解读

A confidence sequence is a sequence of confidence intervals that is uniformly valid over an unbounded time horizon. Our work develops confidence sequences whose widths go to zero, with nonasymptotic coverage guarantees under nonparametric conditions. We draw connections between the Cramér–Chernoff method for exponential concentration, the law of the iterated logarithm (LIL) and the sequential probability ratio test—our confidence sequences are time-uniform extensions of the…

概率测度间熵正则最优传输随机算法的渐近分布与收敛速率AI 解读

This paper is devoted to the stochastic approximation of entropically regularized Wasserstein distances between two probability measures, also known as Sinkhorn divergences. The semi-dual formulation of such regularized optimal transportation problems can be rewritten as a nonstrongly concave optimisation problem. It allows to implement a Robbins–Monro stochastic algorithm to estimate the Sinkhorn divergence using a sequence of data sampled from one of the two distributions.…

利用图根分布表示网络AI 解读

Exchangeable random graphs serve as an important probabilistic framework for the statistical analysis of network data. In this work, we develop an alternative parameterization for a large class of exchangeable random graphs, where the nodes are independent random vectors in a linear space equipped with an indefinite inner product, and the edge probability between two nodes equals the inner product of the corresponding node vectors. Therefore, the distribution of exchangeable…

未知干扰下的平均处理效应AI 解读

We investigate large-sample properties of treatment effect estimators under unknown interference in randomized experiments. The inferential target is a generalization of the average treatment effect estimand that marginalizes over potential spillover effects. We show that estimators commonly used to estimate treatment effects under no interference are consistent for the generalized estimand for several common experimental designs under limited but otherwise arbitrary and…

一般测量误差分布假设下的密度反卷积AI 解读

In this paper, we study the problem of density deconvolution under general assumptions on the measurement error distribution. Typically, deconvolution estimators are constructed using Fourier transform techniques, and it is assumed that the characteristic function of the measurement errors does not have zeros on the real line. This assumption is rather strong and is not fulfilled in many cases of interest. In this paper, we develop a methodology for constructing optimal…

高维协变量观察性研究中的重叠性AI 解读

Estimating causal effects under exogeneity hinges on two key assumptions: unconfoundedness and overlap. Researchers often argue that unconfoundedness is more plausible when more covariates are included in the analysis. Less discussed is the fact that covariate overlap is more difficult to satisfy in this setting. In this paper, we explore the implications of overlap in observational studies with high-dimensional covariates and formalize curse-of-dimensionality argument,…

得分驱动波动率模型的连续时间极限AI 解读

We provide general conditions under which a class of discrete-time volatility models driven by the score of the conditional density converges in distribution to a stochastic differential equation as the interval between observations goes to zero. We show that the form of the diffusion limit depends on: (i) the link function, (ii) the conditional second moment of the score, (iii) the normalization of the score. Interestingly, the properties of the stochastic differential…

扩散Copula：识别与估计AI 解读

We propose a new semiparametric approach for modelling nonlinear univariate diffusions, where the observed process is a nonparametric transformation of an underlying parametric diffusion (UPD). This modelling strategy yields a general class of semiparametric Markov diffusion models with parametric dynamic copulas and nonparametric marginal distributions. We provide primitive conditions for the identification of the UPD parameters together with the unknown transformations…

含主导单元的空间模型估计与推断AI 解读

In spatial econometrics literature estimation and inference are carried out assuming that the matrix of spatial or network connections has uniformly bounded absolute column sums in the number of units, n, in the network. This paper relaxes this restriction and allows for one or more units to have pervasive effects in the network. The linear–quadratic central limit theorem of Kelejian and Prucha (2001) is generalized to allow for such dominant units, and the asymptotic…

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…

观测驱动时间序列模型中的缺失观测AI 解读

We argue that existing methods for the treatment of missing observations in time-varying parameter observation-driven models lead to inconsistent inference. We provide a formal proof of this inconsistency for a Gaussian model with time-varying mean. A Monte Carlo simulation study supports this theoretical result and illustrates how the inconsistency problem extends to score-driven and, more generally, to observation-driven models, which include well-known models for…

重新审视中国FDI区位：异质冲击面板方法AI 解读

Foreign Direct Investment (FDI) is viewed as a primary driving force in shaping the global economy and receives particular attention in empirical studies. In this paper, we argue that many of the existing studies ignore endogeneities that arise from shocks in source and destination countries. To address this endogeneity issue, we take the “controlling through estimating” idea from the econometric literature and propose using panel data models with heterogeneous shocks to…

具有相关随机效应的空间动态面板数据模型AI 解读

In this paper, M-estimation and inference methods are developed for spatial dynamic panel data models with correlated random effects, based on short panels. The unobserved individual-specific effects are assumed to be correlated with the observed time-varying regressors linearly or in a linearizable way, giving the so-called correlated random effects model, which allows the estimation of effects of time-invariant regressors. The unbiased estimating functions are obtained by…

变随机系数模型AI 解读

This paper analyzes unobserved heterogeneity when observed characteristics are modeled nonlinearly. The proposed model builds on varying random coefficients (VRC) that are determined by nonlinear functions of observed regressors and additively separable unobservables. This paper proposes a novel estimator of the VRC density based on weighted sieve minimum distance. The main example of sieve bases are Hermite functions which yield a numerically stable estimation procedure.…

大维动态因子模型：含一阶单整协整因子的脉冲响应估计AI 解读

We study a large-dimensional Dynamic Factor Model where: (i) the vector of factors Ft is I(1) and driven by a number of shocks that is smaller than the dimension of Ft; and, (ii) the idiosyncratic components are either I(1) or I(0). Under (i), the factors Ft are cointegrated and can be modeled as a Vector Error Correction Model (VECM). Under (i) and (ii), we provide consistent estimators, as both the cross-sectional size n and the time dimension T go to infinity, for the…

非参数部分识别下部分线性工具变量模型的稳健与最优估计AI 解读

This paper studies robust and optimal estimation of the slope coefficients in a partially linear instrumental variables model with nonparametric partial identification. We establish the root-n asymptotic normality of a penalized sieve minimum distance estimator of the slope coefficients. We show that the asymptotic normality holds regardless of whether the nonparametric function is point identified or only partially identified. However, in the presence of nonparametric…

动态面板空间向量自回归估计：稳定性与空间多变量协整AI 解读

This paper introduces dynamic panel spatial vector autoregressive models. We study features of dynamics and spatial interactions that an SVAR model can generate and classify the model into stable or unstable cases by partitioning parameter spaces. For stable, spatial cointegration, and mixed cointegration cases, we investigate identification and QML estimation of the models to take into account simultaneity and correlated relationships. Asymptotic properties and…

最小二乘模型平均渐近最优性的新研究AI 解读

In this article, we present a comprehensive study of asymptotic optimality of least squares model averaging methods. The concept of asymptotic optimality is that in a large-sample sense, the method results in the model averaging estimator with the smallest possible prediction loss among all such estimators. In the literature, asymptotic optimality is usually proved under specific weights restriction or using hardly interpretable assumptions. This article provides a new…

期权观测误差中的空间依赖AI 解读

In this paper, we develop the first formal nonparametric test for whether the observation errors in option panels display spatial dependence. The panel consists of options with different strikes and tenors written on a given underlying asset. The asymptotic design is of the infill type—the mesh of the strike grid for the observed options shrinks asymptotically to zero, while the set of observation times and tenors for the option panel remains fixed. We propose a Portmanteau…

稳健化的期望最大生产前沿AI 解读

The aim of this paper is to construct a robust nonparametric estimator for the production frontier. We study this problem under a regression model with one-sided errors, where the regression function defines the achievable maximum output, for a given level of inputs-usage, and the regression error defines the inefficiency term. The main tool is a concept of partial regression boundary defined as a special probability-weighted moment. This concept motivates a robustified…

异方差与多工具变量下的工具变量模型推断AI 解读

This paper proposes novel inference procedures for instrumental variable models in the presence of many, potentially weak instruments that are robust to the presence of heteroskedasticity. First, we provide an Anderson–Rubin-type test for the entire parameter vector that is valid under assumptions weaker than previously proposed Anderson–Rubin-type tests. Second, we consider the case of testing a subset of parameters under the assumption that a consistent estimator for the…