带厚尾新息的非线性自回归函数型条件异方差模型的尾概率

研究带厚尾新息的非线性自回归函数型条件异方差(NARFCH)模型的平稳分布的尾概率. 结果表明, NARFCH序列的平稳分布的尾部紧密依赖于其条件方差. 当新息序列呈厚尾分布时, NARFCH序列的平稳分布的尾部会比新息序列的尾部更厚或更薄, 给出了具体的尾概率的增加或减少对条件方差的依赖公式, 并给出了两个具体例子来说明主要结果的应用.     

随机设计下非参数回归模型方差变点Ratio检验

研究随机设计下非参数回归模型方差变点Ratio检验.首先用局部多项式方法估计回归曲线得到残差序列,其次基于残差的平方序列构造Ratio检验统计量并推导检验统计量的极限分布.最后数值模拟与实例分析结果表明方法的有效性.     

时间序列中方差的结构变点的小波识别(英文)

本文给出了时间序列中方差的小波系数的两种估计:连续估计和离散估计.这两种估计可以用来检测时间序列中方差的结构变点.利用这两种估计我们给出了方差变点的位置和跳跃幅度的估计,并且显示出这些估计可达到最佳收敛速度.同时,我们还给出了这些估计的收敛速度以及检验统计量的渐进分布!     

非参数回归中方差变点的小波检测(英文)

本文主要研究了非参数回归模型中方差函数的变点, 利用小波方法构造的检验量来检测方差中的变点,建立了这些检验量的渐近分布, 并且运用这些检验量构造了方差变点的位置和跳跃幅度的估计, 给出了这些估计的渐近性质, 并进一步通过随机模拟验证了本文方法在有限样本下的性质.     

正态分布方差变点的检验

本文讨论了正态分布方差只有一个变点的检验问题，我们构造了三个检验统计量，其中L检验基于非参数U统计量，B检验基于Bayes方法，R检验由极大似然比方法导出．本文给出了L、B、R检验的渐近临界值，并用MonteCarlo模拟方法研究了这三个检验与平方的CUSUM检验以及LM检验的势，并进行了比较。当变点在序列的前一半位置时，L和R检验较好，当变点在序列的后一半位置时，平方的CUSUM和B检验较好．     

基于Bootstrap的厚尾相依序列持久性变点检验

基于修正方差比率函数给出一种检验厚尾序列持久性变点的统计量.在无变点的假设下得到了统计量的渐近分布.为避免检验渐近分布中的厚尾指数,构造Bootstrap抽样方法来确定渐近分布的经验临界值.数值模拟研究结果说明修正方差比率统计量及Bootstrap抽样方法的有效性.     

沪深股市大盘指数收益率分布尾部的实证研究

通过H ill估计的改进方法对上证综合指数和深圳成分指数的收益率分布的尾部指数进行了参数估计,用χ2检验验证了指数的稳定性及其置信区间.在此基础上提出用尾部指数估计尾概率,达到风险控制的目的.实证研究表明,沪深大盘指数收益率分布具有肥尾的特征,但并不服从无限方差分布.     

基于MCMC算法的贝叶斯变结构分位自回归模型研究

为了更全面细致的刻画时间序列变结构性的特征及其相依性,提出了一类马尔可夫变结构分位自回归模型。利用非对称Laplace分布构建了模型的似然函数,证明了当回归系数的先验分布选择为扩散先验分布时,参数的各阶后验矩都是存在的,并给出了能确定变点位置和性质的隐含变量的后验完全条件分布。仿真分析结果发现马尔可夫变结构分位自回归模型可以全面有效地实现对时间序列数据变结构性的刻画。并应用贝叶斯Markov分位自回归方法分析了中国证券市场的变结构性,结果发现中国证券市场在不同阶段尾部表现出不同的相依性。     

非参数回归模型均值与方差双重变点的估计

本文基于核估计和小波方法研究异方差非参数回归模型中均值函数和方差函数均存在变点的估计问题.首先,构造基于均值函数的核估计量,求出均值变点位置及跳跃度的估计.其次,利用小波方法构造方差变点的估计量,运用该估计量获得方差变点位置与跳跃度的估计,给出变点估计量的渐近性质.最后数值模拟并通过比较验证了方法的有效性.     

结构突变时间序列的贝叶斯单位根检验

在贝叶斯框架下检验含有多个均值与趋势双突变点的时间序列的平稳性。引入标号随机变量表示观测值所处的位置,运用贝叶斯因子模型选择的方法判断结构变点数目,对待检参数的先验设定为混合分布,采用可信区间检验序列是否存在单位根,并用蒙特卡罗模拟验证该方法的有效性,且以中国居民消费价格指数为对象进行实证研究,进一步印证了贝叶斯单位根检验在结构突变时间序列单位根检验中的优越性。研究发现:判断序列是否存在单位根时,不能忽视结构突变问题,否则会产生误判;先验设定对单位根检验影响较大,混合先验优于单一先验;中国居民价格消费指数序列在不考虑结构突变时是不平稳的,若考虑结构突变则该序列平稳;贝叶斯单位根检验克服了经典方法在有限样本单位根检验时存在有偏的问题,提高了单位根检验的功效。     

Spurious regression due to neglected of non-stationary volatility

This paper examines the effects of permanent changes in the variance of the errors on routine applications of standard t-ratio test in regression models. It is shown the asymptotic distribution of t-ratio test is not invariant to non-stationary in variance, and the phenomenon of spurious regression will occur independently of the structure assumed for these time series. The intuition behind this is that the non-stationary volatility can increase persistency in the level of regression errors, which then leads to spurious correlation. Monte Carlo experiment evidence indicates that, in contrast to the broken level/trend case, the presence of spurious relationship critically depends on the location and magnitude of changes, regardless of the sample size. Finally, some real data sets from the Shanghai stock database are reported for illustration.     

Spurious spatial regression with equal weights

This note studies the Lee and Yu (2009) spurious regression model for the special case where the weight matrix is normalized and has equal elements, and where the nonstationarity is caused by near unit roots. It shows that spurious spatial regression will not occur in a spatially autoregressive (SAR) model when the spatial weight matrix is row-normalized and has equal weights. In fact, the asymptotic distribution of the OLS estimate will always converge to its true value zero. The only condition required is that the spatial coefficients of the dependent and independent variables be both less than 1, which is a requirement for the SAR model to be an equilibrium model.     

Spurious modes in finite-element discretizations of the wave equation may not be all that bad

If higher-order finite elements are used to discretize the wave equation, spurious modes may occur. These modes are classified as unphysical and supposedly make elements of high order useless for accurate computations. This is in conflict with numerical experiments which appear to provide good results. Here Fourier analysis is used to investigate the behaviour of the numerical error for a number of higher-order one-dimensional finite elements. It is shown that the spurious modes have a contribution to the numerical error that behaves in a reasonable manner, and that higher-order elements can be more accurate than lower-order elements. Lumped elements with Gauss–Lobatto nodes appear to be the best choice.     

Structural Stability of Discontinuous Galerkin Schemes

The goal of this work is to determine classes of traveling solitary wave solutions for a differential approximation of a discontinuous Galerkin finite difference scheme by means of an hyperbolic ansatz. It is shown that spurious solitary waves can occur in finite-difference solutions of nonlinear wave equation. The occurence of such a spurious solitary wave, which exhibits a very long life time, results in a non-vanishing numerical error for arbitrary time in unbounded numerical domain. Such a behavior is referred here to have a structural instability of the scheme, since the space of solutions spanned by the numerical scheme encompasses types of solutions (solitary waves in the present case) that are not solutions of the original continuous equations. This paper extends our previous work about classical schemes to discontinuous Galerkin schemes (David and Sagaut in Chaos Solitons Fractals 41(4):2193?C2199, 2009; Chaos Solitons Fractals 41(2):655?C660, 2009).     

Spurious regression and lurking variables

We present asymptotic and finite-sample arguments to study the spurious regression problem. This problem may be solved by introducing a lurking variable in the specification even if it is merely a proxy variable. Moreover, this approach is also valid if the lurking variable is a trending mechanism, as when the spurious regression is due to nonstationarities in the variables.     

Heavy loads and heavy tails

The present paper is concerned with the stationary workload of queues with heavy-tailed (regularly varying) characteristics. We adopt a transform perspective to illuminate a close connection between the tail asymptotics and heavy-traffic limit in infinite-variance scenarios. This serves as a tribute to some of the pioneering results of J.W. Cohen in this domain. We specifically demonstrate that reduced-load equivalence properties established for the tail asymptotics of the workload naturally extend to the heavy-traffic limit.     

Default risk analysis via a discrete‐time cure rate model

Cure models represent an appealing tool when analyzing default time data where two groups of companies are supposed to coexist: those which could eventually experience a default (uncured) and those which could not develop an endpoint (cured). One of their most interesting properties is the possibility to distinguish among covariates exerting their influence on the probability of belonging to the populations’ uncured fraction, from those affecting the default time distribution. This feature allows a separate analysis of the two dimensions of the default risk: whether the default can occur and when it will occur, given that it can occur. Basing our analysis on a large sample of Italian firms, the probability of being uncured is here estimated with a binary logit regression, whereas a discrete time version of a Cox's proportional hazards approach is used to model the time distribution of defaults. The extension of the cure model as a forecasting framework is then accomplished by replacing the discrete time baseline function with an appropriate time‐varying system level covariate, able to capture the underlying macroeconomic cycle. We propose a holdout sample procedure to test the classification power of the cure model. When compared with a single‐period logit regression and a standard duration analysis approach, the cure model has proven to be more reliable in terms of the overall predictive performance. Copyright © 2013 John Wiley & Sons, Ltd.     

上证综指的已实现波动率预测模型

采用上证综指2000-2008年的高频数据,在考察了中国股市已实现波动率的特征(即具有长记忆性、结构突变、不对称性和周内效应的特征并且结构突变只能部分解释已实现波动率的长记忆性)的基础上,构建了一个自适应的不对称性HAR-D-FIGARCH模型,并用于波动率的预测。模型的估计结果表明,与其他HAR模型相比,该模型对样本内数据的拟合效果最好。最后,通过SPA检验实证评价和比较了该模型与其他5种已实现波动率预测模型的样本外预测精度。结果发现,在各种损失函数下,该模型是预测中国股市已实现波动率精度最高的模型。     

数据预处理对替代数据检验方法的影响

替代数据检验法是检验时间序列中是否存在确定性非线性成分的重要统计方法.通过研究差分和数据平滑运算对替代数据检验方法的影响,指出常用的线性滤波等数据预处理步骤破坏了序列的静态性质,从而会导致对零假设的错误拒绝.因此,建议应该直接利用原始时间序列而非应用了差分等非静态滤波运算后的时间序列生成替代数据,再进行假设检验,以免造成对零假设的错误拒绝.