基于非参数 ACE变换的贝叶斯非线性协整检验

针对线性以及非线性协整检验存在模型参数过多、小样本条件下检验功效偏低的问题,提出基于非参数 ACE变换的贝叶斯非线性协整 VAR模型, 运用 ACE算法进行变量变换, 结合参数的完全条件分布设计 Gibbs抽样方案, 进行贝叶斯非线性协整检验, 并利用 MonteCarlo仿真研究了贝叶斯非线性协整方法的检验势, 发现贝叶斯非线性协整比经典 Johansen法具有更高更稳健的检验势;同时, 对中国城市和农村居民消费价格指数序列进行实证分析.研究结果表明:贝叶斯非线性协整方法解决了模型中参数过多、小样本条件下检验功效偏低的问题,提高了估计的精确度和检验的准确性     

非线性协整建模研究及沪深股市实证分析

讨论了线性协整和非线性协整的涵义,指出在非线性系统中,非线性协整可以更好地刻画多个时间序列之间的均衡关系.提出了利用小波神经网络逼近非线性协整函数的方法,并给出了训练小波神经网络的变尺度算法.最后利用上海和深圳股指数据进行了实证研究,通过与BP神经网络的比较,证实了小波神经网络在非线性协整建模中的有效性,并说明沪深股市之间存在着非线性协整关系.     

汇率货币模型的非线性协整关系检验——基于深度GRU神经网络

本文采用深度门控循环单元(GRU)神经网络探讨三种汇率货币模型(弹性价格、前瞻性和实际利率差模型)的非线性协整关系。GRU技术在深度学习中具有智能记忆、自主学习和强逼近能力等优点。为此,本文运用该技术对6组典型浮动汇率制国别数据进行了非线性Johansen协整检验。结果表明,汇率与宏观经济基本面之间存在非线性协整关系,从而说明了货币模型在非线性条件下的有效性,以及先进的深度学习工具在检验经济理论中的优势。     

基于贝叶斯机制转换协整模型的石油——股市非对称效应研究

传统门限机制转换模型存在参数估计最优化计算复杂以及参数不可识别的问题,本文构建双机制的贝叶斯门限机制转换协整模型研究国际石油价格与股市之间的非线性动态关系。利用八个亚太股票市场和国际石油价格的数据,结合MCMC抽样算法进行贝叶斯分析,着重考察国际石油—股市之间的非对称效应,结果发现国际石油价格与韩国、马来西亚股市之间具有门限机制转换的非线性协整关系,表明国际石油价格与韩国、马来西亚股市存在非对称效应,而国际石油价格与日本、澳大利亚、印度、印度尼西亚、台湾和新加坡股市之间没有非对称效应。进一步,格兰杰因果检验结果发现,国际石油价格与日本、澳大利亚、韩国、印度、马来西亚、印度尼西亚和新加坡股票价格指数之间存在双向格兰杰因果关系,而与台湾股票价格指数之间没有明显的格兰杰因果关系。     

非线性协整关系的存在性研究

运用神经网络技术 ,提出了一种估计和检验非线性长记忆时间序列之间的协整关系的方法 ,并通过仿真试验说明了所提方法的实用价值 .同时 ,根据马氏过程的遍历理论 ,对网络输出残差的平稳条件进行了研究 ,证明了网络输出平稳残差的充分条件 ,给出了残差序列存在唯一的渐近平稳分布的条件     

基于双正交小波支持向量机的变结构非线性协整研究

基于双正交小波在非线性信号逼近方面的良好性能,构造了一类新的双正交小波核函数并证明了该核函数满足正定核的容许性条件.在此基础上构造了基于双正交小波支持向量机的非线性协整模型,并基于双正交小波支持向量机和最小拟合误差原则,提出针对向量时间序列的最优非线性变结构点检测的动态规划方法.最后,以美元指数和原油、黄金、铜、铝、锌、铅和锡等七种国际大宗商品期货价格为对象,进行变结构非线性协整的实证研究.结果表明,美元指数与七种国际大宗商品期货价格之间具有复杂的变结构非线性关系,双正交小波支持向量机和最优非线性变结构点检测方法在变结构非线性协整分析上是有效的.     

小样本协整系统的检验

采用Mon te Carlo 模拟方法, 给出了小样本容量下协整检验临界值的响应面( respon se su rface) 方程. 结果表明小样本协整检验的临界值不仅与样本容量有关, 还依赖于协整检验中 的滞后阶数. 最后对中国城镇居民的食品消费进行了小样本协整分析.     

基于典型相关分析的无约束综列协整检验

现有的综列协整检验方法均存在较强的约束条件.该文基于Johansen典型相关分析,在允许截面个体间存在协整关系和动态影响的情况下,建立了综列协整的迹检验和最大特征值检验等似然比检验统计量.为了避免似然比检验在有限样本下的分布扭曲,在无约束综列协整检验中引入了Bootstrap程序,以提高检验结论的可靠性.仿真实验结果显示,无约束综列协整检验显著优于Groen和Keibergen的有约束检验.     

人民币购买力平价均衡汇率实证检验——基于VECM协整的因素分析

本文首先对2007年6月至2012年11月的人民币/美元汇率分别进行了严格的和弱化的购买力平价的单位根检验,得出的结论为:在此期间的人民币/美元汇率不存在严格的购买力平价均衡汇率,但存在弱化的购买力平价均衡汇率。在此结论上,本文在此基础上运用协整方法进行实证分析,并运用脉冲响应函数分析了长期均衡因素的短期影响效果。     

我国农村居民消费水平影响因素的计量分析——基于协整检验

本文选取了1978-2009年我国农村居民消费水平及其影响因素的数据,对影响我国农村居民消费水平的主要因素进行了研究,通过变量间的动态关系,建立消费支出及其影响因素之间的长期均衡计量经济模型,并对模型进行了平稳性检验和协整检验,得到了相关模型,并研究了各解释变量对农村居民消费水平的影响程度.     

航线网络中需求无约束估计的非参数选择模型

传统的航空客运需求无约束估计方法仅针对平行直达航班中的顾客需求“溢出”和“再 现”问题,未能考虑航空网络中直达和中转联程航班之间的网络替代效应． 基于顾客偏好排序列表定义了航空网络顾客类型集合,建立了考虑顾客策略行为的网络型非参数离散选择模型．考虑到网络环境下历史预售数据的不完备性,站在线上和线下交易平台的角度,分别建立了非截尾和截尾需求情况下的完备数据对数似然函数． 采用 EM 算法对顾客到达率和概率质量函数进行联合估计,并提出了网络环境下的顾客“初始需求”、“再现需求”和“溢出需求”无约束估计计算方法．通过数值模拟验证了所提方法的可行性和有效性,相较于现有方法能准确反映产品间网络替代效应对顾客选择行为影响,从而更加有效地避免对历史顾客“初始需求”的高估问题．     

泡沫变化过程的动态贝叶斯模型研究

构建了能够刻画资产价格泡沫形成、增长到破灭过程的统计模型.通过估计模型里的时变系数,可以确定泡沫产生的时点和持续时间.该模型也同时刻画了泡沫破灭的概率随着资产价格偏离其基本价值越来越远而变得越来越大的特征.为了对模型进行估计,提出了贝叶斯递归算法和近似算法,并以北京房地产市场为例对模型的应用进行了探讨.     

Bayesian Inference for Hospital Quality in a Selection Model

This paper develops new econometric methods to infer hospital quality in a model with discrete dependent variables and nonrandom selection. Mortality rates in patient discharge records are widely used to infer hospital quality. However, hospital admission is not random and some hospitals may attract patients with greater unobserved severity of illness than others. In this situation the assumption of random admission leads to spurious inference about hospital quality. This study controls for hospital selection using a model in which distance between the patient's residence and alternative hospitals are key exogenous variables. Bayesian inference in this model is feasible using a Markov chain Monte Carlo posterior simulator, and attaches posterior probabilities to quality comparisons between individual hospitals and groups of hospitals. The study uses data on 74,848 Medicare patients admitted to 114 hospitals in Los Angeles County from 1989 through 1992 with a diagnosis of pneumonia. It finds the smallest and largest hospitals to be of the highest quality. There is strong evidence of dependence between the unobserved severity of illness and the assignment of patients to hospitals, whereby patients with a high unobserved severity of illness are disproportionately admitted to high quality hospitals. Consequently a conventional probit model leads to inferences about quality that are markedly different from those in this study's selection model.     

Some Bayesian Experimental Design Theory for Risk Reduction in Extrapolation

Decision problems depending on extrapolation promise to become increasingly important. The key problem is determining if the model being used for extrapolation is going to give reasonable results, or err in a dangerous manner. Ideally, as one proceeds from investigation to decision, some guidance should be present based on the goal as to which investigation will reduce the risk the most given the cost. In this report, a very simple version of the problem is formalized and examined. The result is, interestingly, that the best evidence in support of the favored model is a null result in the experiment most likely to raise doubt over that model. The theory is applied to a simple example drawn from accelerated testing.     

An ACE in the hole: Twin family models for applied behavioral genetics research

Many studies investigate the genetic and environmental influences on traits using twin data and ACE models (A = additive genetic effects, C = shared environment effects, E = unshared environment effects). Unfortunately, relying on twins leads to biased results and limits what researchers can study. We introduce twin models and describe their problems. We show how to solve these problems with data from twins and their families, which allow modeling new effects such as the parent-to-child transmission of traits. We illustrate twin family models using extraversion (extroversion) data from the Virginia 30,000 twin family dataset, giving model specifications and code for the program Mplus. We conclude that if researchers are interested in understanding a broad sense of genetic influences on observed variables, traditional twin models are adequate. However, when data from twins and families are available, twin family models offer better and more interesting estimates of genetic and environmental effects.     

Tracking national and regional spatial-temporal mortality risk associated with NO2 concentrations in Canada: a Bayesian hierarchical two-level model

The association between daily variations in urban air quality and mortality has been well documented using time series statistical methods. This approach assumes a constant association over time. We develop a space-time dynamic model that relaxes this assumption, thus more directly examining the hypothesis that improvements in air quality translate into improvements in public health. We postulate a Bayesian hierarchical two-level model to estimate annual mortality risks at regional and national levels and to track both risk and heterogeneity of risk within and between regions over time. We illustrate our methods using daily nitrogen dioxide concentrations (NO2) and nonaccidental mortality data collected for 1984-2004 in 24 Canadian cities. Estimates of risk and heterogeneity are compared by cause of mortality (cardio-pulmonary [CP] versus non-CP) and season, respectively. Over the entire period, the NO2 risk for CP mortality was slightly lower but with a narrower credible interval than that for non-CP mortality, mainly due to an unusually low risk for a single year (1998). Warm season NO2 risk was higher than cold season risk for both CP and non-CP mortality. For 21 years overall there were no significant differences detected among the four regional NO2 risks. We found overall that there was no strong evidence for time trends in NO2 risk at national or regional levels. However, an increasing linear time trend in the annual between-region heterogeneities was detected, which suggests the differences in risk among the four regions are getting larger, and further studies are necessary to understand the increasing heterogeneity.     

A novel kernel-free nonlinear SVM for semi-supervised classification in disease diagnosis

Journal of Combinatorial Optimization - Semi-supervised classification methods are widely-used and attractive for dealing with both labeled and unlabeled data in real-world problems. In this paper,...