Heteroskedasticity-consistent interval estimators

The linear regression model is commonly used in applications. One of the assumptions made is that the error variances are constant across all observations. This assumption, known as homoskedasticity, is frequently violated in practice. A commonly used strategy is to estimate the regression parameters by ordinary least squares and to compute standard errors that deliver asymptotically valid inference under both homoskedasticity and heteroskedasticity of an unknown form. Several consistent standard errors have been proposed in the literature, and evaluated in numerical experiments based on their point estimation performance and on the finite sample behaviour of associated hypothesis tests. We build upon the existing literature by constructing heteroskedasticity-consistent interval estimators and numerically evaluating their finite sample performance. Different bootstrap interval estimators are also considered. The numerical results favour the HC4 interval estimator.     

Testing for Stochastic Dominance Efficiency

We consider consistent tests for stochastic dominance efficiency at any order of a given portfolio with respect to all possible portfolios constructed from a set of assets. We justify block bootstrap approaches to achieve valid inference in a time series setting. The test statistics are computed using linear and mixed integer programming formulations. Monte Carlo results show that the bootstrap procedure performs well in finite samples. The empirical application reveals that the Fama and French market portfolio is first and second-order stochastic dominance efficient, although it is mean–variance inefficient.     

MSE-matrix superiority of the mixed over the least squares estimator in the presence of outliers

In his recent paper, Ali (1991) has shown that the mixed regression estimator, when data contain mean-shift or variance inflation outliers, is uniformly superior to the ordinary least squares estimator in terms of scalar-valued mean square error. However, when using the matrix-valued mean square error criterion, this dominance fails to hold in general. The subsequent investigation gives a complete characterization of the situation where the mixed estimator is superior to the LS-estimator when the comparison is made with respect to this stronger MSE-property. Vice versa, the LS-estimator never dominates the mixed estimator relative to this criterion.     

A multiple model discrimination procedure

The case of selecting between a set of fixed models is considered. The true model is assumed to be contained in the set of proposed models and errors are taken to be normally distributed. A sequential procedure which yeilds probabilities of incorrect selections is proposed. The procedure is shown to have optimal properties and is extended to the estimated model case by a bootstrap procedure.     

Inference for Time Series Regression Models With Weakly Dependent and Heteroscedastic Errors

Motivated by the need to assess the significance of the trend in some macroeconomic series, this article considers inference of a parameter in parametric trend functions when the errors exhibit certain degrees of nonstationarity with changing unconditional variances. We adopt the recently developed self-normalized approach to avoid the difficulty involved in the estimation of the asymptotic variance of the ordinary least-square estimator. The limiting distribution of the self-normalized quantity is nonpivotal but can be consistently approximated by using the wild bootstrap, which is not consistent in general without studentization. Numerical simulation demonstrates favorable coverage properties of the proposed method in comparison with alternative ones. The U.S. nominal wages series is analyzed to illustrate the finite sample performance. Some technical details are included in the online supplemental material.     

On a nonparametric test for the mixed-model

Nonparametric tests for the null hypothesis of no treatment effect in the mixed-model experiment which involves n randomly chosen subjects who respond once to each of ρ distinct treatments have been developed by Koch and Sen (1968), These tests were based on the assumption of compound symmetry of the error vectors and on the weaker assumption of diagonal symmetry of the error vectors. This paper considers an alternative (permutationally) distribution-free test under this latter assumption. The new test follows the same type of distribution theory as those in Koch and Sen, but utilizes the inherent invariance structure in a more visable and direct way.     

Effect of Gender Composition on Group Performance

The effect of gender composition on group performance was investigated over three years using the MARKSTRAT 2 simulation. Senior undergraduates formed groups and competed within five‐firm (group) industries over 10 weeks, as well as submitting group reports on their performances. Performance and report marks were independent of group size and individual performance in other course assessment. Women per group (numbers, percentage, gender ratio) was positively related to simulation performance, with few women‐dominated groups ranking lower than first or second within each industry. Very few all‐men groups achieved first or second rankings. There were no significant correlations between group gender compositions and marks for the group written report. Women's more interactive, people‐oriented and co‐operative work styles apparently facilitate the MARKSTRAT 2 group process and, when combined with men's more analytical decision‐making tendencies and competitive orientation, appear to explain the superior performances of mixed groups. The emergence and utilization of these tendencies within groups are discussed in terms of Social Contact Theory and Competition Theory. Although generalizations from student‐based studies to the workplace is problematic, the results indicate that groups may be more effective when women outnumber or equal men, especially in complex management activities requiring extensive information management and processing, planning and decision‐making over protracted periods.     

A Nonparametric Test for the Parallelism of Two First-Order Autoregressive Processes

A nonparametric testing procedure for the parallelism of two first-order autoregressive processes is presented. This paper discuss the Mann–Whitney statistic, its natural competitor two-sample t -test, and the bootstrap method. It studies the asymptotic efficacies of the studentized Mann–Whitney statistic and the t -test statistic with their relative efficiency. Simulation results for comparing the powers of these test statistics are also presented.     

新的空洞型损伤演化律及损伤弹塑性大变形有限元描述

本文通过对细观空洞模型的分析，推导出各向同性／运动混合强化材料更为真实和简单的损伤演化和弱化函数的表达式，式中考虑了空洞形状变化对损伤演化的影响，将所得的空洞演化率和弱化函数嵌入新的增量型内本构呆，并考虑损伤引起的塑性体积膨胀，得到了损伤弹塑性大变形内时本构方程，发展相应的轴对称有限元分析程序，用其分析圆柱拉伸试件的颈缩和开有切槽的圆柱试件在切槽前缘的应力和损伤分布，得到与实验较为吻合的结果。     

关于财经类院校英语专业的课程设置及人才培养

复合型英语人才的培养是一种全新的人才培养模式。课程建设是复合型英语人才培养过程中极其重要的环节,因为课程是培养目标的具体体现,是实现人才培养的根本途径。因此,加快教学改革,调整课程设置和教学内容已是当务之急。