Nonlinear Time Series Modelling: An Introduction

Recent developments in nonlinear time series modelling are reviewed. Three main types of nonlinear model are discussed: Markov Switching, Threshold Autoregression and Smooth Transition Autoregression. Classical and Bayesian estimation techniques are described for each model. Parametric tests for nonlinearity are reviewed with examples from the three types of model. Finally forecasting and impulse response analysis is developed.     

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.     

Short Time Series Analysis: C Statistic vs Edgington Model

Young's C statistic (1941) makes it possible to compare the randomization of a set of sequentially organized data and constitutes an alternative of appropriate analysis in short time series designs. On the other hand, models based on the randomization of stimuli are also very important within the behavioral content applied. For this reason, a comparison is established between the C statistic and the Edgington model. The data analyzed in the comparative study have been obtained from graphs in studies published in behavioral journals. According to the results obtained, it is concluded that the Edgington model in experimental designs AB involves many measurements while the C statistic requires fewer observations to reach the conventional significance level.     

含有趋势时间序列的模型分析

本文首先提出两种含有时间趋势的模型，然后论证如果使用不正确模型来分析，将会得出错误的结论。     

对经济增长的时间序列分析

时间序列分析在经济运用中作用十分明显。利用1980～2003年国内生产总值的相关资料,运用时间序列分析,应用SAS软件对经济增长时间序列进行模型识别、拟合、估计和预测,预测结果较为满意。而改革开放以来,投资在经济增长中的作用越来越明显,在对经济增长序列进行时间序列分析的同时,也结合回归分析建立经济增长和投资的回归-时间序列组合模型来进行分析。     

A Review of Nonparametric Time Series Analysis

Various features of a given time series may be analyzed by nonparametric techniques. Generally the characteristic of interest is allowed to have a general form which is approximated increasingly precisely when the sample size goes to infinity. We review nonparametric methods of this type for estimating the spectral density, the conditional mean, higher order conditional moments or conditional densities. Moreover, density estimation with correlated data, bootstrap methods for time series and nonparametric trend analysis are described.     

耦合哈密顿系统的时间序列分析

在研究许多物理模型问题中,人们对系统的具体结构知之甚少,甚至往往不知道其动力学规律,而只是测得与系统性质有关的某一变量随时间变化的数据,这就是所谓时间序列或信号。这被测得的变量可以是系统的状态变量之一,如细胞膜的内外电位差或振动系统沿某一方向的位移,但也可能并不是系统的状态变量而是与系统状态有关的某一变量,如医院里对人从体表测得的脉搏、心电图、脑电图、胃电图、心音和肺音都是如此。在这种情况下,如何由这种时间序列确定系统的运动性质和特征呢?人们用相空间重构和功率谱的方法来对系统的性质和特征进行分析判断。本文章用这两种方法对倾斜磁场下双势阱中粒子的运动模型做了进一步的分析。     

中国地区经济增长的收敛性:时间序列的经验研究

本文运用Bernard & Durlarf (1995,1996)的时间序列分析法,通过对中国东中西部三大地区内和地区间人均产出序列的协整关系检验,从一个新的视角研究了我国地区内和地区间经济增长的收敛性。实证结果表明,东部和西部地区内的经济增长具有收敛性,而中部地区内和三大地区间的经济增长却不存在收敛趋势。另一个发现是中部各省的经济增长在长期受三个共同冲击的影响,东中西部地区的经济增长在长期受两个共同冲击的影响。     

Time Series Analysis of Repeated Surveys: The State–space Approach

Cross sectional estimates from repeated surveys form a time series { y_t }. These estimates can be viewed as the sum y _t = Y _t + e _t of two processes, { Y _t }, the population process and { e _t }, the survey error process. Serial correlations in the latter series are usually present, mainly due to sample overlap. Other sources of data such as censuses, administrative records and demographic population counts are also available. The state–space modelling approach to the analysis of repeated surveys allows combining information from different sources, incorporating benchmarking constraints in a natural way. Results from these methods seem to compare favourably with those from X-11-ARIMA in filtering out survey errors.     

港口集装箱吞吐量时间序列的分形分析

针对港口集装箱吞吐量的非线性特征,引入非线性理论中的分形时间序列分析方法,通过关联维定量表示港口集装箱吞吐量的复杂性,通过hurst指数研究港口集装箱吞吐量的长程相关性.以深圳港的集装箱吞吐量时间序列为例,计算的关联维为2.154 3,hurst指数为0.9367,说明深圳港的集装箱吞吐量存在分形特征和长程相关性.     

正则化的时间序列AR模型及其在经济分析中的应用

通过添加一个正则化因子α,使时间序列AR(n)模型的最小二乘估计(X′X)-1X′Y变为(X′X+αI)-1X′Y,改善了时间序列分析模型中信息矩阵的病态程度,避免了时间序列分析模型产生不适定;经济统计数据分析表明,新的正则化时间序列分析模型在一定程度上起到了稳定所求参数的作用。     

多元回归时间序列方法在货运市场预测中的应用

为了解决制定某地区货运行业发展规划中的区域货运发展规模的分析和预测问题。提出一种以统计数据为依据,为货运行业需求量建立预测模型,并应用SPSS(Statistical Product Service Solution)分析软件,对预测结果进行评估检验的方法。最后以四川货运行业为例,研究预测了四川省今后数年的货运需求量,为四川货运部门进行决策提供一定的科学依据。     

正则化的时间序列AR模型及其在经济分析中的应用

通过添加一个正则化因子OC,使时间序列AR（n）模型的最小二乘估计（X’X）-’X’Y变为（X’X＋αl）-^1X’Y,改善了时间序列分析模型中信息矩阵的病态程度,避免了时间序列分析模型产生不适定;经济统计数据分析表明,新的正则化时间序列分析模型在一定程度上起到了稳定所求参数的作用。     

Bootstrapping Financial Time Series

It is well known that time series of returns are characterized by volatility clustering and excess kurtosis. Therefore, when modelling the dynamic behavior of returns, inference and prediction methods, based on independent and/or Gaussian observations may be inadequate. As bootstrap methods are not, in general, based on any particular assumption on the distribution of the data, they are well suited for the analysis of returns. This paper reviews the application of bootstrap procedures for inference and prediction of financial time series. In relation to inference, bootstrap techniques have been applied to obtain the sample distribution of statistics for testing, for example, autoregressive dynamics in the conditional mean and variance, unit roots in the mean, fractional integration in volatility and the predictive ability of technical trading rules. On the other hand, bootstrap procedures have been used to estimate the distribution of returns which is of interest, for example, for Value at Risk (VaR) models or for prediction purposes. Although the application of bootstrap techniques to the empirical analysis of financial time series is very broad, there are few analytical results on the statistical properties of these techniques when applied to heteroscedastic time series. Furthermore, there are quite a few papers where the bootstrap procedures used are not adequate.     

基于MF-DFA的股票时间序列聚类分析及其应用

多重分形消除趋势波动分析法(MF-DFA)不仅能够去除股票时间序列的长期趋势波动,还能够精确反应股票时间序列的多重分形特性。首先利用MF-DFA方法对股票时间序列进行多重分形分析,结果表明,相比标准多重分析,MF-DFA方法更能反映时间序列的多重分形特性。其次,定义一种以多重分形谱参数作为相似性度量函数的聚类方法对股票时间序列进行聚类。最后,在Markowitz提出的期望均值收益—收益方差(M-V)模型的基础上,把聚类结果运用股票投资组合当中。采用上海证券市场28支股票进行实验验证表明,在给定的收益率下,采用基于多重分形谱参数的聚类方法的股票组合可以得到比随机组合更小的风险水平。     

Time Series Analysis of Strategic Pricing Behavior in the US Airline Industry

A positive relationship betweencompetitive pressure and technical efficiency has been demonstratedby several studies; other studies hold forth that airline marketsbehave strategically. We bring these two literatures togetherby presenting a time series methodology to examine strategicpricing behavior and discussing the implications for airlineefficiency. We find evidence of dynamic, route-level, parallel( i.e. strategic) pricing despite highly variable price structures.A stable price relationship is consistent with successful coordinationof dynamic oligopolists and may highlight those routes wheresignificant market power exists. In light of previous research,this indicates that the airlines on these routes may not be attainingmaximum technical efficiency. For policy makers, this methodologyis useful for analyzing other markets which behave strategically.