Simple Identification and Specification of Cointegrated Varma Models

We bring together some recent advances in the literature on vector autoregressive moving‐average models, creating a simple specification and estimation strategy for the cointegrated case. We show that in this case with fixed initial values there exists a so‐called final moving‐average representation. We prove that the specification strategy is consistent. The performance of the proposed method is investigated via a Monte Carlo study and a forecasting exercise for US interest rates. We find that our method performs well relative to alternative approaches for cointegrated series and methods which do not allow for moving‐average terms. Copyright © 2014 John Wiley & Sons, Ltd.     

Recent Theoretical Results for Time Series Models with GARCH Errors

This paper provides a review of some recent theoretical results for time series models with GARCH errors, and is directed towards practitioners. Starting with the simple ARCH model and proceeding to the GARCH model, some results for stationary and nonstationary ARMA–GARCH are summarized. Various new ARCH–type models, including double threshold ARCH and GARCH, ARFIMA–GARCH, CHARMA and vector ARMA–GARCH, are also reviewed.     

山东省人均GDP时间序列模型的建立

人均GDP的增长具有其内在的规律性。从山东省的实际情况出发,以1978--2002年山东省逐年人均GDP的统计数据为依据,将这些数据进行平稳化、零均值化处理,并利用序列的自相关函数、偏自相关函数的性质,确认序列应当适合的模型,从而建立其时间序列模型。     

A Reduced Rank Regression Approach to Tests of Asset Pricing

Both the Arbitrage Pricing Theory (APT) and the Capital Asset Pricing Model (CAPM) place restrictions of the cross-sectional variation of conditional expectations of asset returns and of macro indicators. We show that these restrictions imposed on the reference statistical models lead to special cases of the reduced rank regression model. The maximum likelihood problem is solved by canonical correlation analysis. Likelihood ratio tests about the number of factors underlying stock returns are straightforward to calculate, thus allowing discrimination between competing financial theories. Moreover LR tests on the relevance of each macroeconomic indicator within a chosen model can be implemented. Some of the tests are illustrated by an application to Italian stock market data.     

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.     

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.     

A Note On Long Memory Time Series

This note presents the fractional integrated processes which are the main models used to describe long memory phenomena.¹ Section 1 briefly defines the concept of fractional integration, shows the fundamental properties and provides a short summary of the estimation methods. Section 2 consists of a survey of their extensions in order to model long-term cycles.     

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.     

Time Series Modelling of Daily Tax Revenues

We provide a detailed discussion of time series modelling of daily data in general and daily tax revenues in particular. The main feature of the daily tax revenue series is the pattern within calendar months. Standard time series methods for seasonal adjustment and forecasting cannot be used since the number of banking days per calendar month varies and because there are two levels of seasonality: between months and within months. We propose a daily time series model based on unobserved components that allows for the classic decomposition into trend, seasonal plus irregular, but it also includes components for intra-monthly, trading-day and length-of-month effects. Such components typically rely on stochastic cubic spline, polynomial and dummy variable functions. State space techniques are used for the recursive computation of the likelihood and forecasts functions with special allowance for irregular spacing. The model is operational for daily forecasting at the Dutch Ministry of Finance. We present the model specification and discuss estimation and forecasting results up to December 1999. A comparative forecast evaluation is also presented.     

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.     

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。     

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。     

复合FDI空间计量模型的估计——基于第三方国家影响因素的研究

目前国内外对FDI的研究主要关注FDI东道国和母国双边的影响因素.本文在此基础上,将第三方国家经空间加权后的影响因素考虑进来,试图为这方面相对缺乏的实证研究提供补充.由于输入国市场间的依赖度与跨国公司的贸易有重要关联,我们将贸易成本(距离)作为空间权重,基于1995～2005年的美国输出FDI和中国输入FDI的面板数据,先忽略空间相关性,用传统的两国家三因素模型进行估计,而后使用近期发展起来的空间面板数据广义矩估计方法来建立新的复合FDI模型,对比两个模型可以发现第三方国家的影响是显著的.     

时间序列相似性度量方法

本文基于时间序列的相似性度量方法提出了一种新的改进方法PAA_divided+SDTW,针对于原始的DTW度量方法时间复杂度大,计算效率低下的问题,本文采用时间序列建模提取时间序列的特征,基于这些特征进行相似性度量.本文所提出的改进方法在很大程度上降低了时间序列相似性度量的效率,相比于原始的DTW方法的精度有所提高.同时,PAA_divided方法相比于原始的PAA方法的误差更小,更精确地保留了时间序列的本质,对PAA的优化也有一定的借鉴意义.     

区间时间序列预测准确度探讨

区间时间序列在决策过程中提供重要的信息,特别是在经济发展、人口政策、规划管理或金融监管等方面,因此如何计算出预测区间的精确度成为一个重要议题。本文提出两种区间预测准确度分析的方法,通过估计预测结果的平均区间误差平方和及平均相对区间误差和,比较不同预测方法的优劣。并由预测区间与实际区间的重叠位置,充分说明预测方法所具有的有效性。这些分析预测区间准确度的方法,将为管理者提供更客观的决策空间。