基于门限协整系统的预测方法研究

某些非平稳时间序列，它们在整个区间上并不存在线性协整关系，而存在非线性协整关系。作者运用门限协整的概念，处理非线性协整问题，把整个区间检验为若干个子区间，分别在每个区间上进行线性协整分析。文中讨论了滚动预测方法。最后，通过上证和深证A股指数的门限协整分析，验证了门限协整系统在预测中的优越性。     

非线性误差校正模型中的阈值协整检验——基于阈值协整向量未知的扩展

Kapetanios等(2006)假定阈值协整向量已知,在误差校正模型中使用指数函数刻画非线性调节效应,并使用F懈统计量检验非线性阈值协整.本文基于Kapetanios等(2006)的模型设定,将阈值协整向量由已知扩展为未知,并借鉴Hansen和Seo(2002)的方法估计阈值协整向量和构造F*NEC统计量检验非线性阈值协整.仿真试验表明:本文方法估计的阈值协整向量具有近似无偏、对称的分布和相对较高的精度,并且其随样本容量的变化特征符合一致性.进一步,在有限样本下,F*NEC 与FNEC的水平扭曲没有显著差异,但F*NEC的检验势高于FNEC.     

协整向量、调节参数同为非线性的阈值协整检验

针对经济变量的长期均衡和短期调节关系可能同时存在非线性的事实,本文扩展现有阈值协整模型,提出了协整向量、调节参数都为非线性的阈值协整模型,并着重探讨了该模型的检验方法。研究表明,在协整关系的检验中,Wald统计量有较好的有限样本性质。在协整关系的非线性检验中,LMW和LMG统计量的水平扭曲和检验势都较好。在调节参数的非线性检验中,当调节参数具有显著的非线性时,LMH统计量表现出较好的有限样本性质。     

分形市场中的汇率协整关系

本文将混沌与分形运用到汇率研究中,运用R/S方法研究了外汇收益率的长记忆性,并将传统的协整理论推广到广义的分数维协整,研究了时间序列之间的长期均衡关系,并以外汇市场中的欧元和新加坡元兑美元的汇率为例进行了实证分析,指出这两种货币收益率服从分形分布,具有相同的分整阶数且二者存在分数维协整关系。     

浅思协整分析在财务分析中的应用

一、简介协整分析20世纪80年代初,Granger提出的协整(Cointegration)概念是处理非平稳时间序列间长期均衡关系的行之有效的方法。如果一个序列经过平稳性检验发现是非平稳序列,但是却可以通过差分来消除它的非平稳性,我们就说这个序列为单整过程(几阶差分就是几阶单     

沪深股市投机泡沫存在性检验的新视角——基于向量误差修正模型的MTAR协整分析

本文通过分析沪证和深证指数及与股票市场密切相关的国民经济运行变量的时间序列,建立向量自回归(VAR)模型,并在此基础上得出了我国股市与经济运行之间的长期均衡关系和短期变动的向量误差修正(VEC)模型。因此引入惯性门限自回归(MTAR)模型,通过检验协整残差的非对称调整假设,对我国股票市场发展是否存在泡沫现象进行进一步分析。针对崔畅等(2006)、王薛等(2008)运用MTAR协整检验对中国股市进行实证研究的MTAR模型平稳性的检验提出了质疑,并用同样的方法以新的视角对沪深股市泡沫重新进行了协整检验,得出了一些结论。     

面板数据非线性阈值协整的检验方法

非线性阈值协整是线性协整的后续发展。本文使用两机制TR模型对Westerlund和Edgerton(2005)的面板数据协整向量结构突变模型进行扩展,提出截距项具有阈值效应、截距项和斜率系数都具有阈值效应的面板数据非线性阈值协整模型。在此基础上,本文进而分别构造Zc、Ztc、Zr、Ztr统计量检验阈值协整,并对上述统计量的极限分布进行了数学推导,发现它们都收敛于随机泛函。仿真实验结果表明,有限样本下上述检验统计量具有较小的水平扭曲和较高的检验势。     

沪深股市投机泡沫存在性检验的新视角——基于向最误差修正模型的MTAR协整分析

本文通过分析沪证和深证指数及与股票市场密切相关的国民经济运行变量的时间序列,建立向量自回归(VAR)模型,并在此基础上得出了我国股市与经济运行之间的长期均衡关系和短期变动的向量误差修正(VEC)模型.因此引入惯性门限自回归(MTAR)模型,通过检验协整残差的非对称调整假设,对我国股票市场发展是否存在泡沫现象进行进一步分析.针时崔畅等(2006)、王薛等(2008)运用MTAK协整检验时中国股市进行实证研究的MTAK模型平稳性的检验提出了质疑,并用同样的方法以新的视角时沪深股市泡沫重新进行了协整检验,得出了一些结论.     

会计收益与市场收益的协整关系研究

本文探索会计信息反映出的会计收益与市场反应的股票收益之间的关系,通过研究会计收益与市场收益两时间序列的协整关系,探讨两者长短期相互影响的关系,最后得出两时间序列之间存在着长期的协整关系,能够相互影响。     

几类非线性协整模型研究综述

协整分析方法经过20多年的发展成为计量经济学界的一个前沿工具,在经济与金融领域得到了广泛的应用。线性协整分析已经成熟,而非线性协整的理论与方法仍在持续研究中。本文回顾了最近20年非线性协整的发展历史,其中包括结构变化、门限非线性、马尔可夫转换和平滑转换等几类非线性协整模型,强调了这些非线性机制的本质区别,总结了已取得的一些重要研究成果,最后对该问题的最新发展动向加以概括。     

A drunk and her dog: a spurious relation? Cointegration tests as instruments to detect spurious correlations between integrated time series

A significant correlation between integrated time series does not necessarily imply a meaningful relation. The relation can also be meaningless, i.e. spurious. Cointegration is sometimes illustrated by the metaphor of ‘a drunk and her dog’. The relation between integrated processes is meaningful, if they are cointegrated. To prevent spurious correlations, integrated series are usually transformed. This implies a loss of information. In case of cointegration, these transformations are no longer necessary. Moreover, it can be shown that cointegration tests are instruments to detect spurious correlations between integrated time series. This paper compares the Dickey–Fuller and the Johansen cointegration test. By means of Monte Carlo simulations, we found that these cointegration tests are a much more accurate alternative for the identification of spurious relations compared to the rather imprecise method of utilizing the R ²-and DW-statistics recommended by some authors. Furthermore, we demonstrate that cointegration techniques are precise methods of distinguishing between spurious and meaningful relations even if the dependency between the processes is very low. Using these tests, the researcher is not in danger of either neglecting a small but meaningful relation or regarding a relation as meaningful which is actually spurious.     

Forecasting using a periodic transfer function: with an application to the UK price of ferrous scrap

The familiar concept of cointegration enables us to determine whether or not there is a long-run relationship between two integrated time series. However, this may not capture short-run effects such as seasonality. Two series which display different seasonal effects can still be cointegrated. Seasonality may arise independently of the long-run relationship between two time series or, indeed, the long-run relationship may itself be seasonal. The market for recycled ferrous scrap displays these features: the US and UK scrap prices are cointegrated, yet the local markets exhibit different forms of seasonality. The paper addresses the problem of using both cointegrating and seasonal relationships in forecasting time series through the use of periodic transfer function models. We consider the problems of testing for cointegration between series with differing seasonal patterns and develop a periodic transfer function model for the US and UK scrap markets. Forecast comparisons with other time series models suggest that forecasting efficiency may be improved by allowing for periodicity but that such improvement is by no means guaranteed. The correct specification of the periodic component of the model is critical for forecast accuracy.     

Inference on the cointegration rank in fractionally integrated processes

For univariate time series we suggest a new variant of efficient score tests against fractional alternatives. This test has three important merits. First, by means of simulations we observe that it is superior in terms of size and power in some situations of practical interest. Second, it is easily understood and implemented as a slight modification of the Dickey–Fuller test, although our score test has a limiting normal distribution. Third and most important, our test generalizes to multivariate cointegration tests just as the Dickey–Fuller test does. Thus it allows to determine the cointegration rank of fractionally integrated time series. It does so by solving a generalized eigenvalue problem of the type proposed by Johansen (J. Econ. Dyn. Control 12 (1988) 231). However, the limiting distribution of the corresponding trace statistic is χ², where the degrees of freedom depend only on the cointegration rank under the null hypothesis. The usefulness of the asymptotic theory for finite samples is established in a Monte Carlo experiment.     

Cointegration Analysis of Seasonal Time Series

This paper reviews various recent approaches to cointegration analysis of seasonal time series. In addition to the usual decisions concerning data transformations and univariate time series properties, it is necessary to decide how seasonal variation is included in the multivariate model and how standard cointegration methods should accordingly be modified. Seasonal cointegration and periodic cointegration methods are discussed, as are some of their recent refinements. An overview of further research topics is also provided.     

COINTEGRATION AND UNIT ROOTS

Abstract. This paper provides an updated survey of a burgeoning literature on testing, estimation and model specification in the presence of integrated variables. Integrated variables are a specific class of non-stationary variables which seem to characterise faithfully the properties of many macroeconomic time series. The analysis of cointegration develops out of the existence of unit roots and offers a generic route to test the validity of the equilibrium predictions of economic theories. Special emphasis is put on the empirical researcher's point of view.     

Applied cointegration analysis in the mirror of macroeconomic theory

Cointegration analyses of macroeconomic time series are often not based on fully specified theoretical models. We use a theoretical model to scrutinize common procedures in applied cointegration analysis. Monte Carlo experiments show that (1) some tests of the cointegration vectors do not work well on series generated by an equilibrium business cycle model; (2) cointegration restrictions add little to forecasting; (3) structural VAR models based on weak long-run restrictions seem to work well. The main disadvantages of cointegration analysis without strong links to economic theory are that it makes it hard to estimate and interpret the cointegration vectors.     

ARIMA融合神经网络的人民币汇率预测模型研究

本文在深入分析了单整自回归移动平均(ARIMA)模型与神经网络(NN)模型特点的基础上,建立了ARIMA融合NN的人民币汇率时间序列预测模型。其基本思想是充分发挥两种模型在线性空间和非线性空间的预测优势,即将汇率时间序列的数据结构分解为线性自相关主体和非线性残差两部分,首先用ARI-MA模型预测序列的线性主体,然后用NN模型对其非线性残差进行估计,最终合成为整个序列的预测结果。通过对三种人民币汇率序列的仿真实验表明,融合模型的预测准确率显著高于包括随机游走模型在内的单一模型的预测准确率,从而证实了融合模型用于汇率预测的有效性。这一结果也表明,人民币汇率市场并不符合有效市场假设,可以通过模型对汇率未来走势做出较准确预测。