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半参数与非参数条件矩模型的筛法 Wald 与 QLR 推断AI 解读

This paper considers inference on functionals of semi/nonparametric conditional moment restrictions with possibly nonsmooth generalized residuals, which include all of the (nonlinear) nonparametric instrumental variables (IV) as special cases. These models are often ill-posed and hence it is difficult to verify whether a (possibly nonlinear) functional is root-n estimable or not. We provide computationally simple, unified inference procedures that are asymptotically valid…

超高维判别分析的无模型特征筛选AI 解读

This work is concerned with marginal sure independence feature screening for ultrahigh dimensional discriminant analysis. The response variable is categorical in discriminant analysis. This enables us to use the conditional distribution function to construct a new index for feature screening. In this article, we propose a marginal feature screening procedure based on empirical conditional distribution function. We establish the sure screening and ranking consistency…

高维样本协方差矩阵线性谱统计中心极限定理的替换原理及检验应用AI 解读

Sample covariance matrices are widely used in multivariate statistical analysis. The central limit theorems (CLTs) for linear spectral statistics of high-dimensional noncentralized sample covariance matrices have received considerable attention in random matrix theory and have been applied to many high-dimensional statistical problems. However, known population mean vectors are assumed for noncentralized sample covariance matrices, some of which even assume Gaussian-like…

不规则间距空间数据的频域经验似然方法AI 解读

This paper develops empirical likelihood methodology for irregularly spaced spatial data in the frequency domain. Unlike the frequency domain empirical likelihood (FDEL) methodology for time series (on a regular grid), the formulation of the spatial FDEL needs special care due to lack of the usual orthogonality properties of the discrete Fourier transform for irregularly spaced data and due to presence of nontrivial bias in the periodogram under different spatial asymptotic…

利用协变量特定患病率信息提高病例对照研究功效AI 解读

Public registration databases and large cohort studies provide vital information on disease prevalence at various levels of a risk factor. This auxiliary information can be helpful in conducting statistical inference in a new study. We aim to develop a statistical procedure that improves the efficiency of the logistic regression model for a case-control study by utilizing auxiliary information on covariate-specific disease prevalence via a series of unbiased estimating…

弱依赖数据下高维矩约束的广义经验似然AI 解读

This paper considers the maximum generalized empirical likelihood (GEL) estimation and inference on parameters identified by high dimensional moment restrictions with weakly dependent data when the dimensions of the moment restrictions and the parameters diverge along with the sample size. The consistency with rates and the asymptotic normality of the GEL estimator are obtained by properly restricting the growth rates of the dimensions of the parameters and the moment…

非经典测量误差下条件矩模型的半参数估计AI 解读

This paper develops a framework for the analysis of semiparametric conditional moment models with endogenous and mismeasured causes, which is of empirical importance. We show that one set of valid instruments is sufficient to control for both endogeneity and measurement errors of the causes of interest, which has been observed in linear parametric models. Two-step consistent estimators of the parameters of interest are proposed. We also show that the proposed estimators are…

横截面相关下 GMM 框架中的渐近 F 检验AI 解读

The paper develops an asymptotically valid F-test that is robust to spatial autocorrelation in a GMM framework. The validity of the F-test is established under mild conditions that can accommodate a wide range of spatial processes. The proposed F-test is very easy to implement, as critical values are from a standard F-distribution. The F-test achieves triple robustness: it is asymptotically valid regardless of the spatial autocorrelation, the sampling region, and the…

含样本选择的乘法误差模型AI 解读

This paper presents a simple approach to deal with sample selection in models with multiplicative errors. Models for non-negative limited dependent variables such as counts fit this framework. The approach builds on a specification of the conditional mean of the outcome only and is, therefore, semiparametric in nature. GMM estimators are constructed for both cross-section data and for panel data. We derive distribution theory and present Monte Carlo evidence on the…

空间权重矩阵内生时的空间自回归模型估计AI 解读

The spatial autoregressive (SAR) model is a standard tool for analyzing data with spatial correlation. Conventional estimation methods rely on the key assumption that the spatial weight matrix is strictly exogenous, which would likely be violated in some empirical applications where spatial weights are determined by economic factors. This paper presents model specification and estimation of the SAR model with an endogenous spatial weight matrix. We provide three estimation…

均衡数量的非参数推断AI 解读

This paper proposes an estimator and develops an inference procedure for the number of roots of functions that are non‐parametrically identified by conditional moment restrictions. It is shown that a smoothed plug‐in estimator of the number of roots is superconsistent under i.i.d. asymptotics, but asymptotically normal under non‐standard asymptotics. The smoothed estimator is furthermore asymptotically efficient relative to a simple plug‐in estimator. The procedure proposed…

非对称独立成分分析的 R 估计AI 解读

Independent component analysis (ICA) recently has attracted much attention in the statistical literature as an appealing alternative to elliptical models. Whereas k-dimensional elliptical densities depend on one single unspecified radial density, however, k-dimensional independent component distributions involve k unspecified component densities. In practice, for given sample size n and dimension k, this makes the statistical analysis much harder. We focus here on the…

非加性经验风险下 Gibbs 后验的一般不等式AI 解读

The Gibbs posterior is a useful tool for risk minimization, which adopts a Bayesian framework and can incorporate convenient computational algorithms such as Markov chain Monte Carlo. We derive risk bounds for the Gibbs posterior using some general nonasymptotic inequalities, which can be used to derive nearly optimal convergence rates and select models to optimally balance the approximation errors and the stochastic errors. These inequalities are formulated in a very…

两步广义矩估计框架下的固定平滑渐近理论AI 解读

This paper develops the fixed-smoothing asymptotics in a two-step generalized method of moments (GMM) framework. Under this type of asymptotics, the weighting matrix in the second-step GMM criterion function converges weakly to a random matrix and the two-step GMM estimator is asymptotically mixed normal. Nevertheless, the Wald statistic, the GMM criterion function statistic, and the Lagrange multiplier statistic remain asymptotically pivotal. It is shown that critical…

广义经验似然估计中的近外生性与弱识别：多矩渐近分析AI 解读

This paper investigates the Generalized Empirical Likelihood (GEL) estimators when there are local violations of the exogeneity condition (near exogeneity) in the case of many weak moments. We also examine the tradeoff between the degree of violation of the exogeneity and the number of nearly exogenous instruments. In this respect, this paper extends many weak moment asymptotics of Newey and Windmeijer (2009a). The overidentifying restrictions test can detect both mild and…

含附带函数的面板数据模型中的一阶差分AI 解读

This note discusses a class of models for panel data that accommodate between‐group heterogeneity that is allowed to exhibit positive within‐group variance. Such a set‐up generalizes the traditional fixed‐effect paradigm in which between‐group heterogeneity is limited to univariate factors that act like constants within groups. Notable members of the class of models considered are non‐linear regression models with additive heterogeneity and multiplicative‐error models…

含极端次序统计量的社会互动模型AI 解读

In this paper, we introduce a social interaction econometric model with an extreme order statistic to model peer effects. We show that the model is a well‐defined system of equations and that it is a static game with complete information. The social interaction model can include exogenous regressors and group effects. Instrumental variables estimators are proposed for the general model that includes exogenous regressors. We also consider distribution‐free methods that use…

估计方程的扩展经验似然AI 解读

We derive an extended empirical likelihood for parameters defined by estimating equations which generalizes the original empirical likelihood to the full parameter space. Under mild conditions, the extended empirical likelihood has all the asymptotic properties of the original empirical likelihood. The first-order extended empirical likelihood is easy to use and substantially more accurate than the original empirical likelihood.

基于经验似然的后验期望AI 解读

Posterior expectation is widely used as a Bayesian point estimator. In this note we extend it from parametric models to nonparametric models using empirical likelihood, and develop a nonparametric analogue of James–Stein estimation. We use the Laplace method to establish asymptotic approximations to our proposed posterior expectations, and show by simulation that they are often more efficient than the corresponding classical nonparametric procedures, especially when the…

与经济理论一致的预测AI 解读

We consider a method for producing multivariate density forecasts that satisfy moment restrictions implied by economic theory, such as Euler conditions. The method starts from a base forecast that might not satisfy the theoretical restrictions and forces it to satisfy the moment conditions using exponential tilting. Although exponential tilting has been considered before in a Bayesian context (Robertson et al. 2005), our main contributions are: (1) to adapt the method to a…

基于经验似然的条件独立检验AI 解读

We construct two classes of smoothed empirical likelihood ratio tests for the conditional independence hypothesis by writing the null hypothesis as an infinite collection of conditional moment restrictions indexed by a nuisance parameter. One class is based on the CDF; another is based on smoother functions. We show that the test statistics are asymptotically normal under the null hypothesis and a sequence of Pitman local alternatives. We also show that the tests possess an…

含噪观测下二次协变差矩阵的估计：局部矩方法与效率AI 解读

An efficient estimator is constructed for the quadratic covariation or integrated co-volatility matrix of a multivariate continuous martingale based on noisy and nonsynchronous observations under high-frequency asymptotics. Our approach relies on an asymptotically equivalent continuous-time observation model where a local generalised method of moments in the spectral domain turns out to be optimal. Asymptotic semi-parametric efficiency is established in the Cramér–Rao sense.…

无需一致估计渐近协方差矩阵的过度识别限制检验AI 解读

We propose new over-identifying restriction (OIR) tests that are robust to heteroskedasticity and serial correlations of unknown form. The proposed tests do not require consistent estimation of the asymptotic covariance matrix and hence avoid choosing the bandwidth in nonparametric kernel estimation. Instead, they rely on the normalizing matrices that can eliminate the nuisance parameters in the limit. Compared with the conventional OIR test, the proposed tests require only…

弱工具变量与协方差约束下的面板结构建模AI 解读

This paper considers estimating a panel data simultaneous equations model under both coefficient and covariance matrix restrictions in a scenario where one or the other set of identifying restrictions may be invalid or may hold only weakly. We study the limiting properties of various estimators in an asymptotic framework, which takes both the cross-sectional dimension N and the time dimension T to infinity. In this setting as in the pure cross-sectional setup, the…