Unit roots versus other types of time heterogeneity,parameter time dependence and superexogeneity

This paper stresses the restrictive nature of the standard unit root/cointegration assumptions and examines a more general type of time heterogeneity, which might characterize a number of economic variables, and which results in parameter time dependence and misleading statistical inference. We show that in such cases ‘operational’ models cannot be obtained, and the estimation of time‐varying parameter models becomes necessary. For instance, economic processes subject to endemic change can only be adequately modelled in a state space form. This is a very important point, because unstable models will break down when used for forecasting purposes. We also discuss a new test for the null of cointegration developed by Quintos and Phillips (1993), which is based on parameter constancy in cointegrating regressions. Finally, we point out that, if it is possible to condition on a subset of superexogenous variables, parameter instability can be handled by estimating a restricted system. Copyright © 2002 John Wiley & Sons, Ltd.     

一族GARCH模型的概率性质

简要回顾了异方差ARCH(GARCH)模型的有关背景,并以此为基础提出了一族广义自回归条件异方差(GARCH)模型hδt-1,然后讨论了这族广义自回归条件异方差(GARCH)模型的严平稳性及遍历性,t=gt-1 ct-1hρ同时给出了该模型存在高阶矩的充分条件,并对这族GARCH模型的一类子模型进行了模拟.     

非线性时间序列建模的混合GARCH方法

在文献[1]的基础上，首次提出混合广义自回归务件异方差（Mixture Generalized Autoregressive Conditional Heteroscedastic Model简记MGARCH）模型；给出并证明了MGARCH模型的一阶平稳性的充分必要条件及二阶平稳性的充分务件；给出该模型参数估计的EM算法：利用BIC定阶准则对MGARCH模型的各成份进行定阶；计算结果表明该模型对金融非线性时间序列中存在的变异率现象具有较强的描述能力，有广阔的应用前景。     

Bias‐corrected bootstrap prediction intervals for autoregressive model: new alternatives with applications to tourism forecasting

This paper proposes the use of the bias‐corrected bootstrap for interval forecasting of an autoregressive time series with an arbitrary number of deterministic components. We use the bias‐corrected bootstrap based on two alternative bias‐correction methods: the bootstrap and an analytic formula based on asymptotic expansion. We also propose a new stationarity‐correction method, based on stable spectral factorization, as an alternative to Kilian's method exclusively used in past studies. A Monte Carlo experiment is conducted to compare small‐sample properties of prediction intervals. The results show that the bias‐corrected bootstrap prediction intervals proposed in this paper exhibit desirable small‐sample properties. It is also found that the bootstrap bias‐corrected prediction intervals based on stable spectral factorization are tighter and more stable than those based on Kilian's stationarity‐correction. The proposed methods are applied to interval forecasting for the number of tourist arrivals in Hong Kong. Copyright © 2010 John Wiley & Sons, Ltd.     

基于VAR模型的汇率变化与中国进出口贸易关系的实证分析

基于人民币汇率变化对进出口贸易影响的理论分析,选取2005年第4季度到2014年第1季度人民币汇率与进出口贸易的季度数据,经过平稳性、协整关系等计量经济学检验,建立VECM模型,从长期均衡的角度分析了汇率与进出口贸易总额的长期弹性,并通过误差纠正机制考查了偏离均衡路径后回复到均衡路径的短期调整速度。根据实证分析结果提出了促进中国对外贸易健康发展的相关建议。     

Interval Time Series Analysis with an Application to the Sterling-Dollar Exchange Rate

Traditional econometrics has long employed ＂points＂ to measure time series data. In real life situations, however, it suffers the loss of volatility information, since many variables are bounded by intervals in a given period. To address this issue, this paper provides a new methodology for interval time series analysis. The concept of ＂interval stochastic process＂ is formally defined as a counterpart of ＂stochastic process＂ in point-based econometrics. The authors introduce the concepts of interval stationarity, interval statistics （including interval mean, interval variance, etc.） and propose an interval linear model to investigate the dynamic relationships between interval processes. A new interval-based optimization approach for estimation is proposed, and corresponding evaluation criteria are derived. To demonstrate that the new interval method provides valid results, an empirical example on the sterling-dollar exchange rate is presented.     

建立系统科学基础理论框架的一种可能途径与若干具体思路(之七)——离散动力系统的密度演化与序列的信息结构

本文是总题目下的第七篇。全文的总目的是试图从现代物理、分子物学与脑神经解剖等学科领域的最新实验事实，以及相应的前沿理论领域围绕着演化概念研究的展开所获得的已有理念与成就为基础，按照“由大爆炸理论所描述的物理世界之最初情景出现以来，世界物质总是在其不同时空点具体结构状态下的几种基本相互作用属性形成的制约机械造成的物质——能量结构与分布仍非完全平衡态势的推动下，不断地一层一层完成其全方位整体性进一步精细平衡结构，实现其该层次从无序到有序的起伏演化——这一总体自然法则”的认识主线，提出一种建立系统科学基础理论的定性定量框架思路与若干细节方法。文中对相互作用、进化、演化、适应性与复杂性等概念进行了分析，对突变、分歧、吸引子、混沌、协同、分形等基于此种理论作了一种较为直观的诠释。同时，也将论及所谓信息的本质与其人本意义下的价值概念，特别是她与非线性的密切关系等。当然，这一切均还是初步的，尽管其中的一部分我们也已经获得了一些较为严谨的结果。     

多维门限GARCH模型

提出了一个新的多维ARMA-TGARCH(autoregressive moving average-threshold generalized autoregressive conditional heteroskedasticity)模型,研究了这个模型的结构性质和参数估计的渐近性质.首先,给出了该模型存在严平稳和遍历解,以及平稳解存在高阶矩的条件.其次,在二阶矩存在的条件下,证明了参数拟最大似然估计的相合性.最后,给出了参数拟最大似然估计的渐近正态性.     

分布式声传感器井中背景噪声统计特性分析

分布式声传感器技术(DAS: Distributed Acoustic Sensor)是一种新近提出的高精度勘探数据采集方法,受制于复杂背景噪声的影响,DAS记录处理水平有待进一步提高.针对上述问题,采用基于替代序列的时间序列平稳性检验方法和Lilliefors检验法对DAS记录中复杂背景噪声的平稳性和高斯性进行研究....     

异方差混合双自回归模型-HMDAR

研究了异方差混合双自回归模型(Heteroscedastic mixture double-autoregressive model,HMDAR)。给出了HMDAR模型的平稳性条件。利用ECM(Expectation conditional maximization)算法估计模型的参数,运用BIC(Bayes information criterion)准则选择模型.HMDAR模型分布形式的灵活性使得它能够对具有非对称或多峰分布的序列进行建模。将该模型应用于几个模拟和实际数据集均得到了较为满意的结果。特别是对于波动较大的序列,HMDAR模型能比其它模型更好的捕捉到数据序列的特征。