U-Statistics and Their Asymptotic Results for Some Inequality and Poverty Measures

U-statistics form a general class of statistics that have certain important features in common. This class arises as a generalization of the sample mean and the sample variance, and typically members of the class are asymptotically normal with good consistency properties. The class encompasses some widely used income inequality and poverty measures, in particular the variance, the Gini index, the poverty rate, the average poverty gap ratios, the Foster-Greer-Thorbecke index, and the Sen index and its modified form. This paper illustrates how these measures come together within the class of U-statistics, and thereby why U-statistics are useful in econometrics.     

Consistency for the LS estimator in the linear EV regression model with replicate observations

In this paper, we consider the following linear errors-in-variables regression model: ${\xi }_{ij}=x_i+{\delta }_{ij},{\eta }_{ij}=y_i+{\epsilon }_{ij}=\theta +\beta x_i+{\epsilon }_{ij}$, with independent identically distributed errors $({\epsilon }_{ij},{\delta }_{ij}),(j=1,2,\dots ,n_i;i=1,2,\dots )$. The strong and weak consistency for the LS estimators $\stackrel{?}{\beta }$ and $\stackrel{?}{\theta }$ of the unknown parameters $\beta ,\theta$ in this model are obtained, which weaken some known conditions and improve some known results.     

An Efficient Estimation for Switching Regression Models: A Monte Carlo Study

This article investigates an efficient estimation method for a class of switching regressions based on the characteristic function (CF). We show that with the exponential weighting function, the CF-based estimator can be achieved from minimizing a closed form distance measure. Due to the availability of the analytical structure of the asymptotic covariance, an iterative estimation procedure is developed involving the minimization of a precision measure of the asymptotic covariance matrix. Numerical examples are illustrated via a set of Monte Carlo experiments examining the implementation, finite sample property and the efficiency of the proposed estimator.     

On the Preliminary Test Backfitting and Speckman Estimators in Partially Linear Models and Numerical Comparisons

Przystalski and Krajewski (2007 Przystalski , M. , Krajewski , P. ( 2007 ). Constrained estimators of treatment parameters in semiparametric models . Statist. Probab. Lett. 77 : 914 – 919 .[Crossref], [Web of Science ®] , [Google Scholar]) proposed the restricted backfitting (RBCF) estimator and restricted Speckman (RSPC) estimator for the treatment effects in a partially linear model when some additional exact linear restrictions are assumed to hold. In this article, we introduce the preliminary test backfitting (PTBCF) estimator and preliminary test Speckman (PTSPC) estimator when the validity of the restrictions is suspected. Performances of the proposed estimators are examined with respect to the mean squared error (MSE) criterion. In addition, numerical behaviors of the proposed estimators are illustrated and compared via a Monte Carlo simulation study.     

Estimation of Main Effect When Covariates Have Non-Proportional Hazards

The Cox proportional hazards (PH) regression model has been widely used to analyze survival data in clinical trials and observational studies. In addition to estimating the main treatment or exposure group effect, it is common to adjust for additional covariates using the Cox model. It is well known that violation of the PH assumption can lead to estimates that are biased and difficult to interpret, and model checking has become a routine procedure. However, such checking might focus on the primary group comparisons, and the assumption can still be violated when adjusting for many of the potential covariates. We study the effect of violation of the PH assumption of the covariates on the estimation of the main group effect in the Cox model. The results are summarized in terms of the bias and the coverage properties of the confidence intervals. Overall in randomized clinical trials, the bias caused by misspecifying the PH assumption on the covariates is no more than 15% in absolute value regardless of sample size. In observational studies where the covariates are likely correlated with the group variable, however, the bias can be very severe. The coverage properties largely depend on sample size, as expected, as bias becomes dominating with increasing sample size. These findings should serve as cautionary notes when adjusting for potential confounders in observational studies, as the violation of PH assumption on the confounders can lead to erroneous results.     

Likelihood Ratio Order of m-Spacings in Multiple-Outlier Models

This article deals with the multiple-outlier exponential model. The likelihood ratio order between m-spacings of the combined sample is developed, some results extend the conclusions on simple spacings in Wen et al. (2007 Wen , S. , Lu , Q. , Hu , T. ( 2007 ). Likelihood ratio order of spacings of heterogeneous exponential random variables . J. Multivariate Anal. 98 : 743 – 756 .[Crossref], [Web of Science ®] , [Google Scholar]).     

数字经济对我国市域农业经济韧性的影响效应与机理分析——基于系统GMM模型与动态门槛模型的检验

基于2011—2019年我国277个市域农业的面板数据,运用反事实方法科学测度了其经济韧性水平,并借助系统GMM模型、中介模型和动态门槛模型等方法,实证检验了数字经济对市域农业经济韧性的影响效应及作用机理。研究发现：数字经济显著增强了市域农业经济韧性,这一结论经一系列稳健性检验后仍能成立;机制分析结果表明,农业资本深化是数字经济提升市域农业经济韧性的重要路径。异质性分析表明,在东部、中部、西部地区和人力资本水平较高的地区,数字经济对市域农业经济韧性的影响效应更为显著,而对东北部地区的提升效应则不显著。动态门槛检验进一步揭示,数字经济对市域农业经济韧性的影响呈现边际递增的非线性特征,当数字经济发展水平越过特定门槛值（0147）时,数字经济对市域农业经济韧性的促进作用显著增强。     

“功能性表达性模型”与中国警察信任——基于全国样本的多重中介与调节模型分析

功能性模型认为，提升警察信任的主要方法在于预防和控制犯罪，降低犯罪率与提升公众安全感；而表达性模型认为，提升警察信任更应注重维护社会正义与社区凝聚力。基于提出促进中国警察信任度提高的理论模型，提升公众获得感、幸福感、安全感，实现国家和社会整体安全的最终目的，采用结构方程模型验证“功能性-表达性模型”与中国警察信任关系的结果表明，功能性模型与表达性模型内部因素之间存在显著路径关系，同时功能性模型显著影响表达性模型指标进而改变公众对警察的信任程度。经调节效应分析后发现，主观社会阶层负向调节被害经历到犯罪恐惧感的直接路径，社交媒体使用负向调节犯罪恐惧感到警察信任的直接路径；在主观社会阶层和社交媒体使用的共同调节作用下，“被害经历→犯罪恐惧感→警察信任”中介路径效应值，随社交媒体使用频率的提升而增大，随主观社会阶层的升高而减小。     

Genetic distance,international experience and the performance of cross-border R&D for EMNEs

Cross-border R&D can contribute to the enhancement of independent innovation capabilities of emerging markets multinational enterprises (EMNEs) by benefiting from knowledge management. However, scant research exists examining the location impact of cross-border R&D for EMNEs on performance implications. This paper fills this important theoretical gap by building upon the literature of genetic distance in connection with knowledge management. We use a panel data of Chinese high-tech listed companies to empirically examine the impact of genetic distance on the performance of cross-border R&D and the role played by international experience. Our results reveal a positive relationship between genetic distance and the performance of cross-border R&D. Importantly, we highlight the motivation for cross-border R&D of EMNEs to acquire technical knowledge magnifies the positive effects of genetic distance and performance. Furthermore, our analysis indicates that international experience significantly enhanced the positive effect of genetic distance on cross-border R&D performance. We conclude this paper by discussing theoretical contributions to genetic distance, international management and knowledge management, as well as practical implications for cross-border R&D of EMNEs.