城镇化、工业化对农业全要素生产率增长的影响研究——来自重庆37个县(区)面板数据的证据

文章基于2000—2016年重庆37个县（区）面板数据，使用序列DEA方法测算了重庆农业全要素生产率变动，在此基础上探讨了重庆农业全要素生产率增长的时间演变，并从理论和实证层面系统考察了城镇化和工业化对农业全要素生产率增长的影响。结果表明：首先，2000—2016年间重庆市农业全要素生产率呈上升趋势，2007年后重庆市农业全要素生产率增长更为明显，并且重庆市各地区呈现出明显的空间不平衡性。其次，动态面板模型的实证结果表明城镇化对农业全要素生产率增长具有显著正向影响，工业化对农业全要素生产率增长也具有显著正向影响。最后，研究还发现基础设施、教育水平以及金融发展程度对农业全要素生产率增长均具有显著正向影响，而产业结构对农业全要素生产率增长具有显著负向影响。     

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.     

Values and happiness among Asian adolescents: a cross-national study

Despite policy efforts to increase adolescent happiness, their impact has been unsatisfactory. Their limited impact may be rooted from a discrepancy between values that adolescents pursued and those that the policies were based on. To provide policy implications, our study aims to identify prevailing values for South Korean, Japanese, and Chinese adolescents and to examine the relationship between the values and self-rated happiness (SRH). A cross-sectional study was conducted using survey data collected on approximately 2000 middle and high school students (7th to 12th school grade) from each country in 2008. Firstly, an explanatory factor analysis was conducted to identify salient adolescent values from each country. Subsequently, a multiple logistic regression analysis was conducted for each population group to examine the relationship between the identified values and adolescent SRH after controlling for sociodemographic characteristics. We found that benevolence and altruism were positively associated with adolescent SRH in all three population groups. Patriarchy was associated with SRH positively in Chinese yet inversely in Japanese. Success pursuit was inversely associated with SRH in Korean. Policy efforts based on values of communities or social harmony may benefit adolescents’ SRH in these three countries.     

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.     

Three Recommendations for Improving the Use of p-Values

ABSTRACT

Researchers commonly use p-values to answer the question: How strongly does the evidence favor the alternative hypothesis relative to the null hypothesis? p-Values themselves do not directly answer this question and are often misinterpreted in ways that lead to overstating the evidence against the null hypothesis. Even in the “post p?<?0.05 era,” however, it is quite possible that p-values will continue to be widely reported and used to assess the strength of evidence (if for no other reason than the widespread availability and use of statistical software that routinely produces p-values and thereby implicitly advocates for their use). If so, the potential for misinterpretation will persist. In this article, we recommend three practices that would help researchers more accurately interpret p-values. Each of the three recommended practices involves interpreting p-values in light of their corresponding “Bayes factor bound,” which is the largest odds in favor of the alternative hypothesis relative to the null hypothesis that is consistent with the observed data. The Bayes factor bound generally indicates that a given p-value provides weaker evidence against the null hypothesis than typically assumed. We therefore believe that our recommendations can guard against some of the most harmful p-value misinterpretations. In research communities that are deeply attached to reliance on “p?<?0.05,” our recommendations will serve as initial steps away from this attachment. We emphasize that our recommendations are intended merely as initial, temporary steps and that many further steps will need to be taken to reach the ultimate destination: a holistic interpretation of statistical evidence that fully conforms to the principles laid out in the ASA statement on statistical significance and p-values.     

地震后家庭幸福指数研究

2008年5月12日，举国震动的汶川大地震打破了平静，灾区人民的生命财产遭受巨大损失。灾后重建，八方支援，灾区人民找回失去的幸福。文章采用问卷调查地震灾区重建后家庭幸福感状况，从物质生活幸福感、环境幸福感和精神生活幸福感三因子反映地震后家庭幸福指数，构建因子模型，以期向爱心人士提供灾后重建的家庭幸福感的可靠信息。     

Noninformative priors for linear combinations of normal means with unequal variances

For normal populations with unequal variances, we develop matching priors and reference priors for a linear combination of the means. Here, we find three second-order matching priors: a highest posterior density (HPD) matching prior, a cumulative distribution function (CDF) matching prior, and a likelihood ratio (LR) matching prior. Furthermore, we show that the reference priors are all first-order matching priors, but that they do not satisfy the second-order matching criterion that establishes the symmetry and the unimodality of the posterior under the developed priors. The results of a simulation indicate that the second-order matching prior outperforms the reference priors in terms of matching the target coverage probabilities, in a frequentist sense. Finally, we compare the Bayesian credible intervals based on the developed priors with the confidence intervals derived from real data.     

Psychometric properties of the “parent-child relationship inventory” (PCRI) in a portuguese forensic sample

In Portugal, there is a gap regarding psychological tests validated for forensic contexts, particularly those related to child custody issues. The Parent–Child Relationship Inventory (PCRI) is one of the most used instruments in child custody contexts. This study aimed to analyze the psychometric properties of PCRI in a Portuguese forensic sample. PCRI factorial structure and psychometric properties were analyzed in a sample of 144 parents involved in child custody assessments. The questionnaire showed good internal consistency, except for the parental support, autonomy, and role orientation scales. It also showed good discriminant capacity. The confirmatory factor analysis did not replicate the 7-factor model proposed by Gerard. The results were discussed based on the use of PCRI in the context of child custody assessment.     

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。     

基于Hawkes因子模型的股价共同跳跃研究

本文首先比较了三种目前主流的共跳检验方法：基于LM检验的共跳检验、BLT共跳检验和FHLL共跳检验，结果表明，三种方法在识别共跳数量上差距明显，但三者结果的重合部分基本属于市场暴涨暴跌行情，说明共跳识别对市场剧烈波动的聚集性较为敏感。基于跳跃、共跳存在的聚集性问题，本文将Hawkes过程引入跳跃和共跳的研究，构建了基于Hawkes过程的因子模型，结果显示，基于Hawkes因子模型的MJ统计量、CJ统计量和实证数据的拟合程度较好，表明因子模型能够更好地描述跳跃和共跳的聚集性。