社会经济地位与教育回报率的无条件分位回归分析——基于CGSS2010年调查数据

无条件分位回归可以在不同分位点上提取教育对收入的一般边际影响。本文使用FFL的无条件分位回归技术,分析了CGSS2010年调查数据,综合父亲的最高教育程度、母亲的最高教育程度和本人认为的14岁时的家庭社会等级三个变量,使用因子分析方法度量了家庭社会经济地位,并根据不同的家庭社会经济地位分别进行了教育回报率的无条件分位回归分析,得出了家庭社会经济地位的不同正在加大群体性的教育回报率差异的结论。     

Composite hierachical linear quantile regression

Multilevel （hierarchical） modeling is a generalization of linear and generalized linear modeling in which regression coefficients are modeled through a model, whose parameters are also estimated from data. Multilevel model fails to fit well typically by the use of the EM algorithm once one of level error variance （like Cauchy distribution） tends to infinity. This paper proposes a composite multilevel to combine the nested structure of multilevel data and the robustness of the composite quantile regression, which greatly improves the efficiency and precision of the estimation. The new approach, which is based on the Gauss-Seidel iteration and takes a full advantage of the composite quantile regression and multilevel models, still works well when the error variance tends to infinity, We show that even the error distribution is normal, the MSE of the estimation of composite multilevel quantile regression models nearly equals to mean regression. When the error distribution is not normal, our method still enjoys great advantages in terms of estimation efficiency.     

基于分层贝叶斯时空模型的雾霾天气过程计数分析

本文以北京市8个行政区(东城区、西城区、石景山区、海淀区、朝阳区、昌平区、顺义区、怀柔区)的PM2.5指数计算各区逐月雾霞天气过程计数频数为研究对象,选择考虑包括地表温度、相对湿度、平均风速、SO_2质量浓度和NO_2质量浓度在内的5个影响因素。本文定义雾霾天气过程,构建分层贝叶斯时空模型,在一个统计模型中对诸多影响因素进行分析,并从计数分析的角度对北京市雾霾天气现象的时空分布、影响因素进行深入讨论。通过分析得出,温度、湿度、污染物浓度对于雾霾天气过程发生具有促进作用,平均风速对于雾霾天气过程发生具有抑制作用。从时空角度分析,从时间维度上看雾霾天气过程的发生具有明显的季节性特征,冬季(1月、2月)以及3月雾霾天气过程发生次数最高,春季(4月、5月)发生次数最低,秋季发生次数略高于夏季。从空间维度上来看,中心城区(东城区、西城区、石景山、海淀区、朝阳区)雾霾天气过程发生次数明显高于郊区(顺义、昌平、怀柔),以东城区、西城区和朝阳区最为严重。     

稀疏VAR在股票收益率研究的应用

向量自回归模型(VAR)广泛应用在对时间相依的多元时间序列建模中,但在高维数据建模中,自回归的系数膨胀可能导致噪音估计、不稳定的预测、解释上的困难等问题。在实际应用中,序列的真实模型往往具有稀疏性,因此运用稀疏VAR模型对高维时间序列进行建模,不仅可以解决高维数据带来的上述困难,也有利于寻找高维数据内在的真实模型。本文以10家公司的股票收益率为研究对象,采用3种不同的稀疏估计方法,不但分析了股票收益率之间的动态关系,而且通过实证分析展示了稀疏估计的优势。     

数据分组和右截尾情形下混合指数分布的参数估计

混合模型是可靠性工程,金融保险和计量经济学等领域中的一类重要模型。本文利用EM算法考虑了混合指数分布在分组数据和右截尾情形下的参数估计问题,并给出了相应的参数估计公式,最后的数值模拟表明EM算法对我们的模型是有效的。     

A new generalized linear exponential distribution and its applications

A new generalized linear exponential distribution （NCLED） is considered in this paper which can be deemed as a new and more flexible extension of linear exponential distribution. Some statistical properties for the NGLED such as the hazard rate function, moments, quantiles are given. The maximum likelihood estimations （MLE） of unknown parameters are also discussed. A simulation study and two real data analyzes are carried out to illustrate that the new distribution is more flexible and effective than other popular distributions in modeling lifetime data.     

Adaptive local linear quantile regression

In this paper we propose a new method of local linear adaptive smoothing for nonparametric conditional quantile regression. Some theoretical properties of the procedure are investigated. Then we demonstrate the performance of the method on a simulated example and compare it with other methods. The simulation results demonstrate a reasonable performance of our method proposed especially in situations when the underlying image is piecewise linear or can be approximated by such images. Generally speaking, our method outperforms most other existing methods in the sense of the mean square estimation (MSE) and mean absolute estimation (MAE) criteria. The procedure is very stable with respect to increasing noise level and the algorithm can be easily applied to higher dimensional situations.     

Momentum Effect Differs Across Stock Performances:Chinese Evidence

Prior empirical studies find positive and negative momentum effect across the global nations, but few focus on explaining the mixed results. In order to address this issue, we apply the quantile regression approach to analyze the momentum effect in the context of Chinese stock market in this paper. The evidence suggests that the momentum effect in Chinese stock is not stable across firms with different levels of performance. We find that negative momentum effect in the short and medium horizon （3 months and 9 months） increases with the quantile of stock returns. And the positive momentum effect is observed in the long horizon （12 months）, which also intensifies for the high performing stocks. According to our study, momentum effect needs to be examined on the basis of stock returns. OLS estimation, which gives an exclusive and biased result, provides misguiding intuitions for momentum effect across the global nations. Based on the empirical results of quantile regression, effective risk control strategies can also be inspired by adjusting the proportion of assets with past performances.     

面板数据分位回归中适应性LASSO调节参数的选择

在带有罚函数的变量选择中,调节参数的选择是一个关键性问题,但遗憾的是,在大多数文献中,调节参数选择的方法较为模糊,多凭经验,缺乏系统的理论方法.本文基于含随机效应的面板数据模型,提出分位回归中适应性LASSO调节参数的选择标准惩罚交叉验证准则(PCV),并讨论比较了该准则与其他选择调节参数的准则的效果.通过对不同分位点进行模拟,我们发现当残差E来自尖峰分布和厚尾分布时,该准则能更好地估计模型参数,尤其对于高分位点和低分位点而言.选取其他分位点时,PCV的效果虽稍逊色于Schwarz信息准则,但明显优于A1kaike 信息准则和交叉验证准则.且在选择变量的准确性方面,该准则比Schwarz信息准则、Akaike信息准则等更加有效.文章最后对我国各地区多个宏观经济指标的面板数据进行建模分析,展示了惩罚交叉验证准则的性能,得到了在不同分位点处宏观经济指标之间的回归关系.     

Poisson分布下基于鞍点逼近的慢性病风险差的置信区间构造

风险差是流行病学中重要的指标之一,常用来比较两种治疗或两种诊断的有效性.因此,风险差区间的精确估计对流行病病情的诊断以及治疗方案的选择有很重要的意义.结合Poisson抽样的优点以及慢性病发病周期长和发病率低的特点,利用鞍点逼近方法来构造了Poisson分布下风险差的置信区间.同时,通过实例和Monte Carlo模拟对传统的四种区间构造方法进行评价.模拟结果表明:在小样本情况下,鞍点逼近方法得到的置信区间大多数能保证覆盖率近似于期望的置信水平并且使得区间长度最短,是一种很好的置信区间构造方法.