住房价格上涨与其影响因素之间的关系研究——基于VEC模型的实证分析

通过建立向量误差修正模型,对中国近年来住房价格持续上涨的原因进行了分析。结果表明,土地价格的快速上涨、金融对房地产业的过度支持以及在住房价格继续上涨预期刺激下的投机因素是中国住房价格上涨过快的主要原因。近年来中国住房价格的持续快速上涨在很大程度上是一种泡沫现象。     

农村居民消费对我国经济增长影响分析

本文通过建立面板数据模型测度了农村居民消费对我国经济增长的影响效应,认为农村居民消费对我国经济增长具有正向促进作用但不够明显,分析了扩大农村居民消费需求促进经济增长所面临的障碍因素并提出了相应的对策建议。     

文化创意产业的金融抑制现状研究

近年来文化创意产业逐渐成为新时期国民经济增长的新动力,然而由于金融发展水平低下,文化创意产业出现了金融抑制现象。应用MRW模型对中国文化创意产业金融抑制现象进行实证分析,发现文化创意产业价值评估难是导致金融抑制的主要原因。应完善金融机构文化创意产业评估机制,对文化创意产品的内在价值进行合理的评估,进而减缓文化创意产业的金融抑制现象的发生。     

能源价格波动对中国物价水平的潜在与实际影响

目的/意义原油作为重要的工业原料、大宗商品和战略储备物资,其价格波动对全球经济发展具有重大影响,对原油价格进行预测是能源经济领域的热点研究课题。对国际原油价格预测的主要方法进行系统梳理并展望未来研究方向,有助于该研究领域的纵深发展。设计/方法基于295篇国际期刊文献,对国际油价预测的发展阶段、发表期刊、研究机构等进行归纳总结,然后对近二十年油价预测领域的主要研究方法进行系统梳理和分析,最后对油价预测工作进行评述和趋势展望。结论/发现自2008年金融危机后,油价预测研究快速发展,相关文章主要发表于能源经济领域的权威期刊《Energy Economic》。现有油价预测方法主要包括计量模型、机器学习和混合预测模型。展望未来,利用高频日内交易数据,融合计量和机器学习前沿方法,对原油价格进行区间预测或概率密度预测是值得探索的方向,此外还应深化油价预测结果的实际应用价值。     

三支决策模型及其研究现状分析

目的/意义二分类群体决策问题广泛存在于社会民生和区域经济中的各个领域,例如：公务员面试、酒店推荐以及交通出行路线选择等。因此,需要提出合理的二分类群体决策方法,从而为政府和相关企业提供决策支持。设计/方法在考虑个体后悔规避行为的基础上,建立二分类选择过程,获得个体和群体关于备选方案的二分类向量;进一步建立二分类共识过程,协助个体调整偏好,从而获得具有较高共识水平的二分类。结论/发现从最小调整成本视角,建立了后悔规避行为下二分类群体决策方法,更加贴近现实决策情景,进一步丰富了群体决策方法的应用范围。     

“十三五”开局之年以信息化推动教育现代化新发展——2016年中国教育信息化十大热点新闻解读

目的/意义随着人工智能时代的到来,在高校教学中进行学生学习动机激励机制的探索,寻找激励学生学习动力的课堂教学方法,有利于适应新时代大学教育改革,打造在线开放“金课”,助力高教质量提升。设计/方法基于互联网移动教学平台在本科课堂教学中实施翻转课堂,从ARCS四个维度设计课堂教学模式,构建四维融合学习动机理论视阈下移动平台翻转课堂教学效果影响因素模型,并利用结构方程模型进行实证分析。结论/发现基于ARCS理论设计的翻转课堂教学模式,能够对高校学生学习动机产生积极影响,提升教学满意度并改善教学效果。移动互联网与教育的深度融合,为高校实施“在线金课”教改提供更多的解决思路和实践探索,推动教育技术进步,增强教育活力和创新力。     

Catholicism, Job Security Regulations and Female Employment: A Micro-level Analysis of Esping-Andersen's Social Catholicism Thesis

This article explores the religious roots of job security regulations in Western democracies by putting Esping-Andersen's famous social Catholicism thesis to the test. Esping-Andersen argues that due to religious socialization, Catholics exhibit more conservative family values, i.e. they tend to support the male breadwinner model. To sustain this model of family organization, Catholics are expected to support job security regulations. These regulations have been described in the literature as important determinants of gender-segregated labour markets and low female employment rates. Data from this article show that while Catholics indeed support more conservative family models, so do Reformed Protestants and religious persons in general. Furthermore, no relationship between religious denomination and preferences for job security regulations can be observed. The analysis thus refutes Esping-Andersen's social Catholicism thesis.     

网络内容的技术控制模式建构与评析

基于维纳的经典控制模式,依照接入控制、过滤控制、编辑控制、分析控制四个流程,尝试建构了网络内容的技术控制模式。此模式着眼于复杂的网络传播环境,反映了多传播时代的多向反馈,体现了效果/批判的范式融合,综合了立体多元的控制手段。     

Cox regression analysis in presence of collinearity: an application to assessment of health risks associated with occupational radiation exposure

This paper considers the analysis of time to event data in the presence of collinearity between covariates. In linear and logistic regression models, the ridge regression estimator has been applied as an alternative to the maximum likelihood estimator in the presence of collinearity. The advantage of the ridge regression estimator over the usual maximum likelihood estimator is that the former often has a smaller total mean square error and is thus more precise. In this paper, we generalized this approach for addressing collinearity to the Cox proportional hazards model. Simulation studies were conducted to evaluate the performance of the ridge regression estimator. Our approach was motivated by an occupational radiation study conducted at Oak Ridge National Laboratory to evaluate health risks associated with occupational radiation exposure in which the exposure tends to be correlated with possible confounders such as years of exposure and attained age. We applied the proposed methods to this study to evaluate the association of radiation exposure with all-cause mortality.     

ELICITING A DIRECTED ACYCLIC GRAPH FOR A MULTIVARIATE TIME SERIES OF VEHICLE COUNTS IN A TRAFFIC NETWORK

The problem of modelling multivariate time series of vehicle counts in traffic networks is considered. It is proposed to use a model called the linear multiregression dynamic model (LMDM). The LMDM is a multivariate Bayesian dynamic model which uses any conditional independence and causal structure across the time series to break down the complex multivariate model into simpler univariate dynamic linear models. The conditional independence and causal structure in the time series can be represented by a directed acyclic graph (DAG). The DAG not only gives a useful pictorial representation of the multivariate structure, but it is also used to build the LMDM. Therefore, eliciting a DAG which gives a realistic representation of the series is a crucial part of the modelling process. A DAG is elicited for the multivariate time series of hourly vehicle counts at the junction of three major roads in the UK. A flow diagram is introduced to give a pictorial representation of the possible vehicle routes through the network. It is shown how this flow diagram, together with a map of the network, can suggest a DAG for the time series suitable for use with an LMDM.