Multivariate stable ARMA processes with time dependent coefficients

This paper considers the class of m-variate autoregressive moving average (ARMA) processes with stable innovations and time dependent coefficients. A set of suitable AR and MA regularity conditions is given to ensure existence and uniqueness of valid solutions. A simple form of the above solution is expressed in terms of one sided Green's matrix functions associated with the AR operator. We solve the prediction problem arising in this class of models. A few examples are added to support the general theory.     

A characterization of vector autoregressive processes with common cyclical features

This paper presents necessary and sufficient conditions for the existence of common cyclical features in Vector Auto Regressive (VAR) processes integrated of order 0, 1, 2, where the common cyclical features correspond to common serial correlation (CS), commonality in the final equations (CE) and co-dependence (CD). The results are based on local rank factorizations of the reversed AR polynomial around the poles of its inverse. All processes with CS structures are found to present also CE structures and vice versa. The presence of CD structures, instead, implies the presence of both CS and CE structures, but not vice versa. Characterizations of the CS, CE, CD linear combinations are given in terms of linear subspaces defined in the local rank factorizations.     

ARMA模型及其应用

人们在不断的实践和认识的过程中 ,产生了一系列分析和研究时间序列的方法及模型。 ARMA模型就是近代时序分析中最为推崇的模型之一     

Metrisable assessment of the course of stream-systemic processes in vector form in industry 4.0

Quality & Quantity - The goal of this paper is to present an innovative conception how to use metrisable vector structure of a manufacturing process, based on quantitative relations between the...     

Bayesian Comparison of ARIMA and Stationary ARMA Models

Time series analysts have long been concerned with distinguishing stationary generating processes from processes for which differencing is required to induce stationarity. In practical applications, this issue is addressed almost invariably through formal hypothesis testing. In this paper, we explore some aspects of the Bayesian approach to the problem, leading to the calculation of posterior odds ratios. Interesting features arise in the simplest possible variant of the problem, where a choice has to be made between a random walk and a stationary first order autoregressive model. We discuss in detail the analysis of this case, and also indicate how our approach extends to the more general comparison of an ARIMA model with a stationary competitor.     

The invertibility of sampled and aggregated ARMA models

Summary Necessary and sufficient conditions for a sampled (resp. aggregated) stationary ARMA process to be invertible are derived. It is shown that a sampled (resp. aggregated) invertible stationary ARMA process is always invertible.     

Fully Bayesian analysis of ARMA time series models

Statistical analysis of autoregressive-moving average (ARMA) models is an important non-standard problem. No classical approach is widely accepted; legitimacy for most classical approaches is based solely on asymptotic grounds, while small sample sizes are common. The only obstacle to the Bayesian approach are designing a structure through which prior information can be incorporated and designing a practical computational method. The objective of this work is to overcome these two obstacles. In addition to the standard results, the Bayesian approach gives a different method of determining the order of the ARMA model, that is (p, q).     

荔波县年降水的ARMA预测模型

文章应用时间序列分析方法,对贵州省荔波县多年降水系列进行分析,将其分解为趋势项、周期项和平稳随机项,建立了时间序列分解预测模型,实例验证结果令人满意,表明了该模型的有效性和适用性,其预测的结果可以用作荔波县旱情预测的参考依据.     

荔波县年降水的ARMA预测模型

文章应用时间序列分析方法,对贵州省荔波县多年降水系列进行分析,将其分解为趋势项、周期项和平稳随机项,建立了时间序列分解预测模型,实例验证结果令人满意,表明了该模型的有效性和适用性,其预测的结果可以用作荔波县旱情预测的参考依据。     

A remark on the recursive estimation of ARMA order

Summary It is well known how, for an ARMA process of order (p ₀,q ₀), max (p ₀,q ₀) may be recursively estimatedHannan/Rissanen. Assuming max (p ₀,q ₀) to be known and, in addition,p ₀q ₀, a simple procedure for the recursive estimation of (p ₀,q ₀) is presented.     

湖南省地区生产总值ARMA分析

运用计量经济学原理对湖南省1952年以来地区生产总值进行动态分析,建立了ARMA模型,并利用历史数据论证模型的正确性,研究地区生产总值变化趋势和特征,给出了地区生产总值的预测方法,为经济决策提供依据。     

基于ARMA模型的公务轿车故障规律分析

基于某油田车管中心公务轿车故障历史记录,应用计量经济学技术对轿车故障记录数据进行分析,寻找轿车故障规律;不无创新地视故障的发生为时间序列进行分析研究、模型预测;建立了不同车型故障时间序列的ARMA模型,并以帕萨特、奥迪A6、君威三种公务车车型为例进行了实证。     

FIML estimation of the dynamic simultaneous equations model with ARMA disturbances

A method is presented for the estimation of the parameters in the dynamic simultaneous equations model with vector autoregressive moving average disturbances. The estimation procedure is derived from the full information maximum likelihood approach and is based on Newton-Raphson techniques applied to the likelihood equations. The resulting two-step Newton-Raphson procedure involves only generalized instrumental variables estimation in the second step. This procedure also serves as the basis for an iterative scheme to solve the normal equations and obtain the maximum likelihood estimates of the conditional likelihood function. A nine-equation variant of the quarterly forecasting model of the US economy developed by Fair is then used as a realistic example to illustrate the estimation procedure described in the paper.     

利用改进的ARMA模型来预测供应链中的需求

本文介绍了ARMA模型的基本原理，分析了ARMA模型在市场需求预测中的不足，进行改进。并通过应用实例说明了改进的ARMA模型应用于需求预测过程的具体步骤和实施难点。     

基于ARMA模型的上市公司盈余质量判断

会计盈余因其具有较高的信息含量已被广泛认可,但是在不同年份,特别在差异的资本市场环境下,存在质量上的差异,造成中小股东及潜在投资者对公司及目标公司前后会计期间的盈余质量不易做出准确的判断。因此我们从会计前后期盈余之间的相关性及波动性来判断盈余质量,通过采用案例分析的方法,并利用Eviews软件进行模型检验。结果表明：基于ARMA的模型建立,对上市公司的前后期盈余质量之间关系具有较高的解释力度,比较好的反映出公司盈余数据的持续相关性及波动性。     

Berlin transformations: another view

Books reviewed in this article: Hartmut Häußermann and Andreas Kapphan, Berlin: von der geteilten zur gespaltenen Stadt? Sozialräumlicher Wandel seit 1990 Stefan Krätke and Renate Borst, Berlin: Metropole zwischen Boom und Krise     

Derivation of the unconditional state-covariance matrix for exact maximum-likelihood estimation of ARMA models

Analytical expressions for the unconditional state-covariance matrix are combined with an efficient scheme for computing theoretical autocovariances, to simplify the exact maximum-likelihood estimation of multivariate ARMA models. Alternative state-space representations for pure AR and pure MA processes, giving rise to straightforward expressions for their unconditional state covariance, are suggested.     

Moving shift averages for ergodic transformations

The equality between the expectation value of a random variable and its shift average under an ergodic transformation is shown·to hold true if the end points of the interval along which the shifts are taken do not increase faster than a linear function of the interval length. If the increase is too fast, however, the moving shift average may, with probability 1, not converge to the expectation value.     

基于ARMA（p，q）模型的企业年金精算研究

传统的保险精算理论为了简化计算,往往假定利率是确定的。但由于企业年金是一种长期的经济行为,投保期间的政府政策、经济周期等因素都会造成利率的不确定性,从而随机利率下企业年金理论的研究逐渐成为保险精算学研究的重点与热点问题之一。本文利用时间序列理论,将常用的利息力模型AR(p)和利息力模型MA(q)进行推广,对各年的利息力δi(i=1,2,…)建立广义条件下的ARMA(p,q)模型,得出此类模型下企业年金的精算现值。最后,根据所建立的模型和所得到的精算现值进行了实例计算分析。