Complex Latent Variable Modeling in Educational Assessment

Bayesian item response theory models have been widely used in different research fields. They support measuring constructs and modeling relationships between constructs, while accounting for complex test situations (e.g., complex sampling designs, missing data, heterogenous population). Advantages of this flexible modeling framework together with powerful simulation-based estimation techniques are discussed. Furthermore, it is shown how the Bayes factor can be used to test relevant hypotheses in assessment using the College Basic Academic Subjects Examination (CBASE) data.     

Spanish migrants to European Union countries: predictors of psychological adjustment / Españoles que emigran a países de la Unión Europea: predictores de su ajuste psicológico

In recent years, the migration of Spanish people to other countries in Europe has increased; however, there are hardly any studies on this phenomenon. The aim of this study is to identify the predictive sociodemographic and psychosocial factors of psychological adjustment (life satisfaction and absence of internalizing symptoms) of emigrants and to determine the indirect effects of these factors through perceived stress. This study also seeks to examine whether the psychological adjustment of Spanish emigrants was similar to Spanish non-emigrants. The sample was made up of a group of Spanish emigrants to Germany and England (n = 858) and an equivalent control group of Spanish non-emigrants (n = 328). The results show that fulfilment of expectations, emotional support and job quality are the best predictors of perceived stress and psychological adjustment. These factors are confirmed to have indirect effects on psychological adjustment through perceived stress. Finally, the mental health of emigrants and non-emigrants was similar, and the implications of these findings are discussed.     

Patience moderates the class cleavage in demand for redistribution

Previous studies on class voting have yielded mixed results linking income and demand for redistribution. Why do some poor people oppose redistribution, while some rich people support it? This article argues that an individual's level of patience, an important personal characteristic that influences how people calculate immediate and distinct outcomes, may moderate the effect of class on redistributive preference. In a one-shot game, redistribution between the rich and the poor is zero sum. When people extend their time horizons, however, the poor see the possibility of upward mobility, while the rich emphasize future losses, such as unemployment and economic instability. Consistent with the hypotheses, analyses of the 2014 Cooperative Congressional Election Study and a representative Taiwanese dataset from 2016 reveal a clear class cleavage in demand for redistribution among impatient poor and rich respondents, but the cleavage between their patient counterparts diminished. This pattern of convergence extends previous studies on upward mobility and risk perception theory.     

动态视角下中国城市群土地利用TFP测度及影响因素研究

提高城市群土地利用TFP对合理高效利用土地资源,协调好城市群经济发展、资源配置和环境保护具有重大理论和实践意义.文章运用DEA-Malmquist指数对2003—2016年动态视角下中国城市群土地利用TFP进行测度和时空演变分析,并利用Tobit回归模型对影响城市群土地利用TFP变化的因素进行深入研究.结果表明:从时间维度看,城市群土地利用TFP年均上升0.7％,城市群土地利用TFP增长由技术效率和技术进步"双核驱动",技术效率的提升主要与纯技术效率提升有关,规模效率是技术效率和土地利用TFP提升的瓶颈约束;从空间维度看,晋中、哈长、海峡西岸、中原、长江中游、北部湾、成渝、黔中、关中平原和兰西城市群土地利用TFP呈下降趋势,其余城市群土地利用TFP均呈上升趋势,且京津冀、呼包鄂榆、辽中南、长江三角洲、山东半岛和天山北坡城市群土地利用TFP增长为技术进步和技术效率"双核驱动";从影响因素看,人均GDP和年末金融机构人民币各项贷款余额对中国城市群土地利用TFP具有正向促进作用,城市财政支出占本市GDP的比重和人口密度具有负向阻碍作用.     

Role of Temperament,Parenting Behaviors,and Stress on Turkish Preschoolers’ Internalizing Symptoms

Child‐ and family‐related factors that predict internalizing symptoms are understudied in preschool years and have a negative influence on children's functioning. We examined observational assessments of preschoolers' temperamental fearfulness and exuberance, mother reports of negative control, warmth, and parenting stress in a sample of 109 Turkish preschoolers. High temperamental fearfulness and low joyful/exuberant positive affectivity in addition to low warmth and high parenting stress had significant effects on internalizing symptoms. Parenting stress had both direct and indirect relations to internalizing symptoms via lower maternal warmth. When comorbid elevations in externalizing symptoms were controlled, the results were consistent with the interpretation that poor parenting practices and stress associated with the parenting role predict maladaptation in general but that the specific form of maladaptation may be best predicted by individual differences in children's temperamental characteristics. This study contributes to our understanding of risk and protective factors that predict preschoolers' internalizing symptoms with a sample from a non‐Western population. These findings can guide early prevention and intervention programs to address internalizing problems in a culturally‐sensitive way.     

On the mathematics of the Jeffreys–Lindley paradox

This paper is concerned with the well known Jeffreys–Lindley paradox. In a Bayesian set up, the so-called paradox arises when a point null hypothesis is tested and an objective prior is sought for the alternative hypothesis. In particular, the posterior for the null hypothesis tends to one when the uncertainty, i.e., the variance, for the parameter value goes to infinity. We argue that the appropriate way to deal with the paradox is to use simple mathematics, and that any philosophical argument is to be regarded as irrelevant.     

基于动态因子Copula模型的行业间系统性风险分析

国际金融市场间的相关关系以及系统性风险受到很多学者的重视，本文则以我国股市的行业指数作为研究对象进行实证研究。通过构建动态因子Copula模型，文章对行业的日收益率数据进行了动态相关性分析，并基于风险预期占比度量了我国行业之间系统性风险的溢出效应。本文分析了2006年1月4日至2016年7月1日的28个行业指数数据，基于GAS动态负荷因子的变化路径来刻画其相关关系，通过风险预期占比来研究行业间的风险溢出效应。研究表明，各个行业指数收益率之间存在较强的关联性。就单个行业来说，化工行业与其他行业关系最为不稳定。就金融与非金融行业而言，金融行业对非金融行业的影响较大且较为平稳。本文所得研究结果可以为投资者和风险管理者在进行决策时提供一定的指导。     

Fixed T dynamic panel data estimators with multifactor errors

This article analyzes a growing group of fixed T dynamic panel data estimators with a multifactor error structure. We use a unified notational approach to describe these estimators and discuss their properties in terms of deviations from an underlying set of basic assumptions. Furthermore, we consider the extendability of these estimators to practical situations that may frequently arise, such as their ability to accommodate unbalanced panels and common observed factors. Using a large-scale simulation exercise, we consider scenarios that remain largely unexplored in the literature, albeit being of great empirical relevance. In particular, we examine (i) the effect of the presence of weakly exogenous covariates, (ii) the effect of changing the magnitude of the correlation between the factor loadings of the dependent variable and those of the covariates, (iii) the impact of the number of moment conditions on bias and size for GMM estimators, and finally (iv) the effect of sample size. We apply each of these estimators to a crime application using a panel data set of local government authorities in New South Wales, Australia; we find that the results bear substantially different policy implications relative to those potentially derived from standard dynamic panel GMM estimators. Thus, our study may serve as a useful guide to practitioners who wish to allow for multiplicative sources of unobserved heterogeneity in their model.     

Mixtures of Gaussian copula factor analyzers for clustering high dimensional data

Mixtures of factor analyzers is a useful model-based clustering method which can avoid the curse of dimensionality in high-dimensional clustering. However, this approach is sensitive to both diverse non-normalities of marginal variables and outliers, which are commonly observed in multivariate experiments. We propose mixtures of Gaussian copula factor analyzers (MGCFA) for clustering high-dimensional clustering. This model has two advantages; (1) it allows different marginal distributions to facilitate fitting flexibility of the mixture model, (2) it can avoid the curse of dimensionality by embedding the factor-analytic structure in the component-correlation matrices of the mixture distribution.An EM algorithm is developed for the fitting of MGCFA. The proposed method is free of the curse of dimensionality and allows any parametric marginal distribution which fits best to the data. It is applied to both synthetic data and a microarray gene expression data for clustering and shows its better performance over several existing methods.