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.     

Bayesian density regression for discrete outcomes

We develop Bayesian models for density regression with emphasis on discrete outcomes. The problem of density regression is approached by considering methods for multivariate density estimation of mixed scale variables, and obtaining conditional densities from the multivariate ones. The approach to multivariate mixed scale outcome density estimation that we describe represents discrete variables, either responses or covariates, as discretised versions of continuous latent variables. We present and compare several models for obtaining these thresholds in the challenging context of count data analysis where the response may be over‐ and/or under‐dispersed in some of the regions of the covariate space. We utilise a nonparametric mixture of multivariate Gaussians to model the directly observed and the latent continuous variables. The paper presents a Markov chain Monte Carlo algorithm for posterior sampling, sufficient conditions for weak consistency, and illustrations on density, mean and quantile regression utilising simulated and real datasets.     

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.     

Family size and educational attainment in England and Wales

In this paper, we use linked census data from England and Wales to investigate whether having a large number of siblings leads to lower educational attainment. There is a large literature suggesting that with large sibship size, parental resources will be diluted and this, in turn, will lead to lower educational attainment. Using twin births and the sex composition of the sibling group as instrumental variables, we find that the evidence of a family size effect on educational attainment is rather uncertain. Similar results are obtained when we use occupational attainment as the dependent variable. We also demonstrate the confounding of birth order and family size effects, and show that an adjusted birth order index proposed by Booth and Kee provides an effective solution to this estimation problem.     

京津冀一体化背景下创新、创业与区域经济增长实证研究

在"大众创业,万众创新"以及京津冀一体化的大背景下,创新、创业成为影响京津冀区域经济增长的重要因素.基于此,构建了衡量区域创新、创业的指标体系,选取京津冀三地2005-2014年省际面板数据,利用工具变量法对京津冀创新、创业与经济增长之间的关系进行回归分析和因子分析,得出京津冀创新、创业整体情况.研究发现,京津冀创新、创业能力对区域经济增长具有正向的影响,并且经济增长速度越快的地区,创新创业的正向效应就越大.     

文献型翻译和工具型翻译——析《Moment in Peking》中社会文化因素的翻译策略

通过对功能主义翻译目的论提出的文献型翻译和工具型翻译进行阐释,结合林语堂Moment in Peking《京华烟云》中社会文化因素的翻译策略,比较这两种翻译策略在文化传播中的作用。指出以传播中国文化为目的时,译者宜主要采用文献型翻译;以提高译文在西方读者中的可读性为目的时,宜主要采用工具型翻译。     

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.     

Fast robust feature screening for ultrahigh-dimensional varying coefficient models

In this paper, we propose a new partial correlation, the so-called composite quantile partial correlation, to measure the relationship of two variables given other variables. We further use this correlation to screen variables in ultrahigh-dimensional varying coefficient models. Our proposed method is fast and robust against outliers and can be efficiently employed in both single index variable and multiple index variable varying coefficient models. Numerical results indicate the preference of our proposed method.