Testing Serial Correlation in Partially Linear Additive Errors-in-variables Models

This article considers testing serial correlation in partially linear additive errors-in-variables model. Based on the empirical likelihood based approach, a test statistic was proposed, and it was shown to follow asymptotically a chi-square distribution under the null hypothesis of no serial correlation. Finally, some simulation studies are conducted to illustrate the performance of the proposed method.     

Comparison of Interval Estimators of Pr(X < Y) in the Two-parameter Exponential Distribution

We consider confidence intervals for the stress–strength reliability Pr(X< Y) in the two-parameter exponential distribution. We have derived the Bayesian highest posterior density interval using non-informative prior distributions. We have compared its performance with the intervals based on the generalized pivot variable intervals in terms of their coverage probabilities and expected lengths. Our simulation study shows that the Bayesian interval performs better according to the criteria used, especially when the sample sizes are very small. An example is given.     

Inferences for a Partially Varying Coefficient Model With Endogenous Regressors

In this article, we propose a new class of semiparametric instrumental variable models with partially varying coefficients, in which the structural function has a partially linear form and the impact of endogenous structural variables can vary over different levels of some exogenous variables. We propose a three-step estimation procedure to estimate both functional and constant coefficients. The consistency and asymptotic normality of these proposed estimators are established. Moreover, a generalized F-test is developed to test whether the functional coefficients are of particular parametric forms with some underlying economic intuitions, and furthermore, the limiting distribution of the proposed generalized F-test statistic under the null hypothesis is established. Finally, we illustrate the finite sample performance of our approach with simulations and two real data examples in economics.     

OLS and IV estimation of regression models including endogenous interaction terms

We analyze a class of linear regression models including interactions of endogenous regressors and exogenous covariates. We show how to generate instrumental variables using the nonlinear functional form of the structural equation when traditional excluded instruments are unknown. We propose to use these instruments with identification robust IV inference. We furthermore show that, whenever functional form identification is not valid, the ordinary least squares (OLS) estimator of the coefficient of the interaction term is consistent and standard OLS inference applies. Using our alternative empirical methods we confirm recent empirical findings on the nonlinear causal relation between financial development and economic growth.     

The Effects of Neighborhood Variables on Needs of Social Care: A Hierarchical Multiple Regression of Senior Residents in China

Community-based elder care has become a dominant model in the world. However, the role of community-level factors in helping or hindering the individuals concerned in obtaining the needed care has not received much attention. This research aims to investigate how community-level factors, including neighborhood disadvantage, residential instability, and social service environment affect the need for instrumental, medical, and emotional services of Chinese senior residents. Employing the hierarchical multiple regression analysis, the results show that instrumental activities of daily living impairment contributed significantly to the need for instrumental, medical and emotional services. Living alone was found to be associated with the need for medical and emotional services. Objective socioeconomic status was only significantly related to the need for medical care. The type of urban community was positively associated with the need for instrumental services and was negatively related to the need for medical care. Residential instability was positively associated with of the need for instrumental services and medical care. Social service environments had heterogeneous effects on the need for social services. However, neighborhood disadvantage had no significant impact on the need for social services. Implications for future research and policy reform were also discussed.     

代际支持关系在老人养老模式选择中的创新作用

随着社会经济结构和家庭结构的变迁,家庭养老功能逐渐弱化,过去由家庭承担的赡养功能逐渐由社会功能来取代。调查证明,老人与子女之间在经济上、求助方式上、精神慰籍上、居住方式上仍然存在着明显的代际支持关系。在进行现代社会养老模式的选择中,应当在充分肯定和发挥家庭养老功能的基础上,积极建立和发展社会养老支持体系。     

土地资源管理专业本科毕业论文选题分析

毕业论文选题是开展论文写作的前提,直接影响论文质量.以2005-2012届沈阳农业大学土地资源管理专业本科生毕业论文为研究对象,从论文选题方向、来源和范围分析土地资源管理专业本科毕业论文选题的特点,在总量上看,土地经济及房地产方向的题目居多,但选题比例出现下降趋势,土地利用工程方向选题增长态势明显.本科毕业论文选题主要来源于教师科研课题,但结合毕业实习选题是新的增长点.学生选题出现大而广、题目越大越好写的误区.因此,创新本科毕业论文指导机制,培养和提高论文指导教师的素养,强化学生选题教育和能力培养,鼓励学生在感兴趣的研究方向选题,选题不能过于陈旧或追求热点问题,选题内容的深度和广度要适宜.     

企业慈善捐赠的前因变量与组织绩效研究

全球经济一体化的加速推进,使传统的生产要素对企业绩效的增强作用显现出了越来越多的局限性,企业必须寻找新的经济增长点和发展战略。在此过程中,作为承担社会责任的一种有效方式,企业慈善捐赠行为的产生与发展具有典型意义。文章运用回归分析等方法,对2008年上海证券交易所有过慈善捐赠的428家上市公司的数据资料进行统计分析与检验,结果表明:企业规模、资产负债率和企业高管人员平均受教育程度均对企业慈善捐赠有影响,但影响程度存在差异;企业通过参与慈善捐赠将获得的无形资源转化为自身的核心优势,促进组织绩效的提升;最后,提出建立有效的政策对鼓励企业参与捐赠的积极意义。     

Nonparametric density deconvolution by weighted kernel estimators

Nonparametric density estimation in the presence of measurement error is considered. The usual kernel deconvolution estimator seeks to account for the contamination in the data by employing a modified kernel. In this paper a new approach based on a weighted kernel density estimator is proposed. Theoretical motivation is provided by the existence of a weight vector that perfectly counteracts the bias in density estimation without generating an excessive increase in variance. In practice a data driven method of weight selection is required. Our strategy is to minimize the discrepancy between a standard kernel estimate from the contaminated data on the one hand, and the convolution of the weighted deconvolution estimate with the measurement error density on the other hand. We consider a direct implementation of this approach, in which the weights are optimized subject to sum and non-negativity constraints, and a regularized version in which the objective function includes a ridge-type penalty. Numerical tests suggest that the weighted kernel estimation can lead to tangible improvements in performance over the usual kernel deconvolution estimator. Furthermore, weighted kernel estimates are free from the problem of negative estimation in the tails that can occur when using modified kernels. The weighted kernel approach generalizes to the case of multivariate deconvolution density estimation in a very straightforward manner.     

A fresh approach for intercession of nonresponse in multivariate longitudinal designs

The occurrence of nonresponse is very much plebeian in surveys, which troubles the analysis, and hence, an inappropriate inference is left out. To counterbalance the sour effects of the incompleteness, fresh imputation techniques have been proposed with the aid of multi-auxiliary variates for the estimation of population mean on successive waves. Properties of the proposed estimators have been elaborated, and they have been compared with the work of Priyanka et al. (2015). Detailed simulation study is carried out to substantiate the empirical and theoretical results. Several possible cases have been addressed in which nonresponse can occur.