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.     

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.     

Model comparison of linear and nonlinear Bayesian structural equation models with dichotomous data

In this article, dichotomous variables are used to compare between linear and nonlinear Bayesian structural equation models. Gibbs sampling method is applied for estimation and model comparison. Statistical inferences that involve estimation of parameters and their standard deviations and residuals analysis for testing the selected model are discussed. Hidden continuous normal distribution (censored normal distribution) is used to solve the problem of dichotomous variables. The proposed procedure is illustrated by a simulation data obtained from R program. Analyses are done by using R2WinBUGS package in R-program.     

Evaluating modified generalized information criterion in presence of multicollinearity

When there are many explanatory variables in the regression model, there is a chance that some of these are intercorrelated. This is where the problem of multicollinearity creeps in due to which precision and accuracy of the coefficients is marred, and the quest to find the best model becomes tedious. To tackle such a situation, Model selection criteria are applied for selecting the best model that fits the data. Current study focuses on the evaluation of the four unmodified and four modified versions of generalized information criteria—Akaike Information Criterion, Schwarz's Bayes Information Criteria, Hannan-Quinn Information Criterion, and Akaike Information Criterion corrected for small samples. A simulation study using SAS software was carried out in order to compare the unmodified and modified versions of the generalized information criteria and to discover the best version amongst the four modified model selection criteria, for identifying the best model, when the collinearity assumption is violated. For the proposed simulation, two samples of size 50 and 100, for three explanatory variables X₁, X₂, and X₃, are drawn from Normal distribution. Two situations of collinearity violations between X₁ and X₂ are looked into, first when ρ = 0.6 and second when ρ = 0.8. The outcomes of the simulations are displayed in the tables along with visual representations. The results revealed that modified versions of the generalized information criteria are more sensitive in identifying models marred with high multicollinearity as compared to the unmodified generalized information criteria.     

The determinacy of the regression factor score predictor based on continuous parameter estimates from categorical variables

The estimation of population parameters of the continuous common factor model from categorical observed variables is meanwhile regularly performed. It is shown that the formula for the calculation of the determinacy of the regression factor score predictor from the estimated model parameters has to be adapted under these conditions. A method for the calculation of this determinacy from the model parameters of the continuous population factor model based on categorical variables is proposed and evaluated by means of simulated population data. It turns out that using the uncorrected formula can lead to serious overestimation of determinacy for categorical variables.