Demography and housing demand—what can we learn from residential construction data?

There are obvious reasons why residential construction should depend on the population’s age structure. We estimate this relation on Swedish time series data and Organization for Economic Cooperation and Development panel data. Large groups of young adults are associated with higher rates of residential construction, but there is also a significant negative effect from those above 75. Age effects on residential investment are robust and forecast well out-of-sample in contrast to the corresponding house price results. This may explain why the debate around house prices and demography has been rather inconclusive. Rapidly aging populations in the industrialized world makes the future look bleak for the construction industry.

国家意识形态语境下的高校思想政治教育

思想政治教育的实质是国家意识形态输出,但这种输出活动面临着国家意识形态和社会生活现象。以及个体经验世界两极化的困难,问题在于国家意识形态疏离了社会生活世界,解决这一问题的办法就在于将国家意识形态社会生活化。本文从历史分析的角度。阐述了思想政治教育的社会生活化演进趋势,并以高等学校思想政治教育为例,分析了当前作为国家意识形态的社会主义核心价值体系在高校这一特定社会生活世界存在的问题．揭示了其中的影响要素,并提出了国家意识形态社会生活化的“五进”路径。     

中国城乡居民年龄别消费模式量化与分析

文章采用2010年中国家庭追踪调查(CFPS)数据量化分析年龄别居民消费模式。针对同类研究只采用年龄变量作为自变量的不足,在经典的需求和消费函数基础上扩展年龄变量构建计量模型,获得了更好的解释力和准确性。基于计量分析结果刻画了分年龄、性别的中国城乡居民消费模式,并进行城乡比较。研究发现,居民家庭消费生命周期中存在30~34岁及45~49岁两个消费高峰;城乡居民消费差距最大的是高龄老人;农村老人在高龄阶段的医疗保健消费明显下降;农村中年妇女的医疗保健支出低于男性。研究认为,应从政策层面有针对性地加大对特定年龄性别人群的扶助力度;同时,未来产业规划应充分考虑人口结构变动因素的影响。     

Quantitative Analysis on the Impact of Urbanisation on Chinese Population Aging

This paper systematically studies the impact of fertility, mortality, initial age structure and rural-urban migration on population aging in rural and urban China from 2000 to 2010. The results show that urbanisation plays a crucial role in population aging in both rural and urban areas and its inf luence is closely linked to the age pattern of the migrants. One third of young rural population transformed into urban population during that period. The contribution of rural-urban migration to population aging in rural areas is 43.4 percent, which is higher than any inf luences from population’s natural changes, and is dominant in population aging in the countryside. Rural-urban migration contributes-118.0 percent to population aging in urban areas by reducing the proportion of aged population and its influence was only lower than that of the initial age structure. The impact of urbanisation on population aging in towns is relatively limited. Among factors from population’s natural changes, the inf luence of the initial age structure is higher than those from changes of mortality and fertility. The paper discusses the causes and developmental trends of impact of urbanisation on population aging between rural and urban areas, and addresses some policy recommendations to deal with socio-economic challenges.     

An Adaptive Functional Autoregressive Forecast Model to Predict Electricity Price Curves

We propose an adaptive functional autoregressive (AFAR) forecast model to predict electricity price curves. With time-varying operators, the AFAR model can be safely used in both stationary and nonstationary situations. A closed-form maximum likelihood (ML) estimator is derived under stationarity. The result is further extended for nonstationarity, where the time-dependent operators are adaptively estimated under local homogeneity. We provide theoretical results of the ML estimator and the adaptive estimator. Simulation study illustrates nice finite sample performance of the AFAR modeling. The AFAR model also exhibits a superior accuracy in the forecast exercise of the California electricity daily price curves compared to several alternatives.     

A Bayesian sequential design with binary outcome

Several researchers have proposed solutions to control type I error rate in sequential designs. The use of Bayesian sequential design becomes more common; however, these designs are subject to inflation of the type I error rate. We propose a Bayesian sequential design for binary outcome using an alpha‐spending function to control the overall type I error rate. Algorithms are presented for calculating critical values and power for the proposed designs. We also propose a new stopping rule for futility. Sensitivity analysis is implemented for assessing the effects of varying the parameters of the prior distribution and maximum total sample size on critical values. Alpha‐spending functions are compared using power and actual sample size through simulations. Further simulations show that, when total sample size is fixed, the proposed design has greater power than the traditional Bayesian sequential design, which sets equal stopping bounds at all interim analyses. We also find that the proposed design with the new stopping for futility rule results in greater power and can stop earlier with a smaller actual sample size, compared with the traditional stopping rule for futility when all other conditions are held constant. Finally, we apply the proposed method to a real data set and compare the results with traditional designs.     

An adaptive-to-model test for partially parametric single-index models

Residual marked empirical process-based tests are commonly used in regression models. However, they suffer from data sparseness in high-dimensional space when there are many covariates. This paper has three purposes. First, we suggest a partial dimension reduction adaptive-to-model testing procedure that can be omnibus against general global alternative models although it fully use the dimension reduction structure under the null hypothesis. This feature is because that the procedure can automatically adapt to the null and alternative models, and thus greatly overcomes the dimensionality problem. Second, to achieve the above goal, we propose a ridge-type eigenvalue ratio estimate to automatically determine the number of linear combinations of the covariates under the null and alternative hypotheses. Third, a Monte-Carlo approximation to the sampling null distribution is suggested. Unlike existing bootstrap approximation methods, this gives an approximation as close to the sampling null distribution as possible by fully utilising the dimension reduction model structure under the null model. Simulation studies and real data analysis are then conducted to illustrate the performance of the new test and compare it with existing tests.     

基于中国股票高频交易数据的随机波动建模与应用

本文对近十五年多达17万笔高频交易数据研究发现,早晨9:30股市开盘期间收益回报显著为负值,而在下午3:00收盘前的5分钟集合竞价阶段的收益回报显著为正值,称这种现象为“首尾5分钟现象”.并且日内收益数据具有较为显著的季节效应或周期效应,本文首次提出利用具有季节效应的SVJt-s模型对上证综合指数的5分钟高频交易数据进行建模,并给出模型的两步估计方法.由于高频随机波动建模时的数据量巨大、计算负荷严重,模型的估计、评价以及预测评价方法都需进行相应的改进,本文主要通过APF方法计算边际似然和BF进行模型比较,并从模型的预测能力发现本文给出的具有季节效应的SVJt-s模型,优于通常的GARCH模型和基本随机波动模型,最后给出了模型在风险管理中的应用.     

我国异质性产业体系低碳化发展的实现路径及其影响因素

本文建立了一个异质性产业体系低碳化发展路径选择的分析框架,通过超越对数形式的随机前沿生产函数,测算了我国29个省区产业体系生产技术及其效率和生产规模效率,以及能源使用技术及其效率和碳减排技术及其效率,并针对不同类型产业体系的低碳化发展提出政策建议.结果表明,我国异质性产业体系可以通过不同的结构升级和技术进步的选择形成适合于自身产业体系特征的低碳化发展路径.     

模糊变权组合预测方法研究

在不确定环境组合预测中,用模糊权重系数更能体现各单项预测方法的客观表现.文章提出一种新的权重系数为三角模糊数的组合预测方法.首先建立以组合预测精确度指数最小为准则的模糊加权组合预测模型,为了避免样本数据中极端值对模型的影响,对模型进行改进,提出带有0-1变量的模糊加权组合预测模型.进一步考虑到单项预测方法在不同时刻的表现有所差异,建立基于诱导有序模糊加权平均(IOFWA)算子的模糊变权组合预测模型,该模型不仅能克服极端值的影响,而且具有更高的预测精确度.并实证验证了该方法的适用性和灵活性.