基于图方法的MA和ARMA模型系数检验

应用图模型方法来讨论传统的MA和ARMA模型,证明了MA和ARMA模型的系数为去掉其他时间序列分量线性效应的条件下的偏相关系数,且利用图模型推断算法提出了一种新的参数估计和检验方法。     

Estimation of rational transfer function models

In the estimation of rational transfer function models, it has been recommended that starting values of a transfer function component be assumed to be zero (or a constant) in the recursive computation of the transfer function response. It is demonstrated that such algorithms may lead to serious bias in the estimation of moving average parameters. This paper discusses several other algorithms that may rectify this problem. It is found that the starting-value-free (SVF) method is a more reliable algorithm. For computer programs using the traditional algorithm, i.e., the zero-starting-value (ZSV) method, the bias problem can be easily remedied using a short-cut method that omits appropriate number of values at the beginning of the residual series.     

我国个体经济就业的实证研究

本文通过回顾我国改革开放以来个体经济就业的发展情况,利用柯布-道格拉斯生产函数建立个体经济的就业函数。并利用1981—2007年个体经济发展的数据建立ARMA模型分析我国个体经济的就业与资金、收入等变量的关京。发现其吸纳就业的能力与营业收入、注册资金总额都成正比。     

Value-at-risk forecasting based on Gaussian mixture ARMA–GARCH model

In this paper, we develop a new forecasting algorithm for value-at-risk (VaR) based on ARMA–GARCH (autoregressive moving average–generalized autoregressive conditional heteroskedastic) models whose innovations follow a Gaussian mixture distribution. For the parameter estimation, we employ the conditional least squares and quasi-maximum-likelihood estimator (QMLE) for ARMA and GARCH parameters, respectively. In particular, Gaussian mixture parameters are estimated based on the residuals obtained from the QMLE of GARCH parameters. Our algorithm provides a handy methodology, spending much less time in calculation than the existing resampling and bias-correction method developed in Hartz et al. [Accurate value-at-risk forecasting based on the normal-GARCH model, Comput. Stat. Data Anal. 50 (2006), pp. 3032–3052]. Through a simulation study and a real-data analysis, it is shown that our method provides an accurate VaR prediction.     

SAS软件的应用——基于ARMA模型的商品销售额的预测分析

文章运用SAS软件系统中的一些时间序列建模方法及回归分析方法对某商品的月销售额作了预测分析,得到了较高的预测精度,在实际应用中预测值的准确对于指导商家的战略决策起着重要作用.     

Allocating Redundancies to k-out-of-n Systems with Independent and Heterogeneous Components

This paper studies the allocation of independent redundancies with a common life distribution to k-out-of-n systems of independent components with non identical life distributions. A sufficient condition is found for allocating more active redundancies to the weaker component to gain a larger lifetime for k-out-of-n systems, and assigning more standby redundancies to the weaker (stronger) components is proved to yield larger lifetime for series (parallel) systems in the sense of the increasing concave (convex) order. Also, the optimal policy is proved to be majorized by all other policies when the system’s components are stochastically ordered.     

基于时间序列分析方法的连续性抽样调查研究

针对连续性抽样调查中如何利用过去各期的调查信息来提高现期抽样估计精度的问题，引入时间序列分析方法，分别考虑连续性抽样调查中重复样本和重叠样本等不同情况，建立了不同情况下的时间序列模型，利用成熟的时间序列分析方法给出了总体特征的线性组合估计量。由于时间序列分析方法能够充分利用以往各期的调查信息，从而能够给出精度更高的估计量。     

A Class of Markov Chain Models for Average Run Length Computations for Autocorrelated Processes

The average run length (ARL) of conventional control charts is typically computed assuming temporal independence. However, this assumption is frequently violated in practical applications. Alternative ARL computations have often been conducted via time consuming and yet not necessarily very accurate simulations. In this article, we develop a class of Markov chain models for evaluating the run length performance of traditional control charts for autocorrelated processes. We show extensions from the univariate AR(1) model to the general multivariate VARMA(p, q) time series. The results of the proposed method are highly comparable to those of simulations and with significantly less computational overhead.     

A NOTE ON ARMA PARAMETERISATION*

This paper considers the relationship between ARMA parameterisations of models for y(t) and Ay(t), where A is invertible and y(t) is a vector time series (t = 0,±1,…). An ARMA model for the transformed series Ay(t) may have fewer parameters than a model for y(t). This paper shows that such a saving is illusory because the apparently saved parameters are exactly balanced by the number of new parameters appearing in A.     

ARCH and Bilinear Time Series Models: Comparison and Combination

Two extensions to the ARMA model, bilinearity and ARCH errors are compared, and their combination is considered. Starting with the ARMA model, tests for each extension are discussed, along with various least squares and maximum likelihood estimates of the parameters and tests of the estimated models based on these. The effects each may have on the identification, estimation, and testing of the other are given, and it is seen that to distinguish between the two properly, it is necessary to combine them into a bilinear model with ARCH errors. Some consequences of the misspecification caused by considering only the ARMA model are noted, and the methods are applied to two real time series.     

Transformed GARMA model: Properties and simulations

Real time series can present anomalies, like non-additivity, non-normality, and heteroscedasticity, which makes using GARMA models impossible. Our article introduces a new class of models called Transformed Generalized Autoregressive Moving Average (TGARMA) models that allow using transformations to guarantee the GARMA assumptions. We present an extensive simulation study of the influence of the transformation on GARMA estimation. We also propose using bootstrap methods to get more information about the distribution of the transformation parameter. We apply the methodology to data related to annual Swedish fertility rates.     

Bounded Area Tests For Comparing The Dynamics Between ARMA Processes

This article presents a new test for discerning whether or not two independent autoregressive moving average (ARMA) processes have the same autocovariance structure. This test utilizes a specific geometric feature of a time series plot, namely the area bounded between the line segments that connect adjacent points and the time axis. It will be shown that if you sample two ARMA processes and calculate the magnitudes of the two resulting bounded areas, then a significant difference among these areas tends to imply a significant difference in autocovariances.     

Generalized normal ARMA model applied to the areas of economy,hydrology, and public policy

The main purpose of this article is the presentation of a new class of time series models which is the merge output of the generalized normal distribution with ideas from the GARMA model. Symmetrically, tails that may be lighter or heavier than the Gaussian distribution, and Gaussian and Laplace distributions as special cases, are the main advantages of the use of generalized normal distribution. The proposed model is called generalized normal autoregressive moving average (GN-ARMA). We exemplify the application of the proposed model adjusting it to the three time series, which are from the areas of economy, hydrology, and public policy.     

Shrinkage estimation for linear regression with ARMA errors

In this paper, we extend the modified lasso of Wang et al. (2007) to the linear regression model with autoregressive moving average (ARMA) errors. Such an extension is far from trivial because new devices need to be called for to establish the asymptotics due to the existence of the moving average component. A shrinkage procedure is proposed to simultaneously estimate the parameters and select the informative variables in the regression, autoregressive, and moving average components. We show that the resulting estimator is consistent in both parameter estimation and variable selection, and enjoys the oracle properties. To overcome the complexity in numerical computation caused by the existence of the moving average component, we propose a procedure based on a least squares approximation to implement estimation. The ordinary least squares formulation with the use of the modified lasso makes the computation very efficient. Simulation studies are conducted to evaluate the finite sample performance of the procedure. An empirical example of ground-level ozone is also provided.     

Semiparametric Time Series Models with Log‐concave Innovations: Maximum Likelihood Estimation and its Consistency

We study semiparametric time series models with innovations following a log‐concave distribution. We propose a general maximum likelihood framework that allows us to estimate simultaneously the parameters of the model and the density of the innovations. This framework can be easily adapted to many well‐known models, including autoregressive moving average (ARMA), generalized autoregressive conditionally heteroscedastic (GARCH), and ARMA‐GARCH models. Furthermore, we show that the estimator under our new framework is consistent in both ARMA and ARMA‐GARCH settings. We demonstrate its finite sample performance via a thorough simulation study and apply it to model the daily log‐return of the FTSE 100 index.     

On Fan's adaptive Neyman tests for comparing two spectral densities

In this paper, we consider tests for assessing whether two stationary and independent time series have the same spectral densities (or same autocovariance functions). Both frequency domain and time domain test statistics for this purpose are reviewed. The adaptive Neyman tests are then introduced and their performances are investigated. Our tests are adaptive, that is, they are constructed completely by the data and do not involve any unknown smoothing parameters. Simulation studies show that our proposed tests are at least comparable to the current tests in most cases. Furthermore, our tests are much more powerful in some cases, such as against the long orders of autoregressive moving average (ARMA) models such as seasonal ARMA series.     

A significance test for classifying arma models

Given that the Euclidean distance between the parameter estimates of autoregressive expansions of autoregressive moving average models can be used to classify stationary time series into groups, a test of hypothesis is proposed to determine whether two stationary series in a particular group have significantly different generating processes. Based on this test a new clustering algorithm is also proposed. The results of Monte Carlo simulations are given.     

Predictive Accuracy Gain From Disaggregate Sampling in ARIMA Models

We compare the forecast accuracy of autoregressive integrated moving average (ARIMA) models based on data observed with high and low frequency, respectively. We discuss how, for instance, a quarterly model can be used to predict one quarter ahead even if only annual data are available, and we compare the variance of the prediction error in this case with the variance if quarterly observations were indeed available. Results on the expected information gain are presented for a number of ARIMA models including models that describe the seasonally adjusted gross national product (GNP) series in the Netherlands. Disaggregation from annual to quarterly GNP data has reduced the variance of short-run forecast errors considerably, but further disaggregation from quarterly to monthly data is found to hardly improve the accuracy of monthly forecasts.     

巨灾风险对非寿险总产出核算影响的研究

 随着中国保险业的迅猛发展,保险核算在一国经济中变得越来越重要。越来越频繁发生的自然灾害、恐怖袭击等造成的巨大损失对联合国国民账户体系(93SNA)推荐的非寿险服务产出核算产生重大冲击,严格按照93SNA算法计算保险服务价值会导致荒谬的结果。首先本文介绍了国际上几种主要的改进方法,对每种方法的优缺点进行了深入分析。这些方法对我国改善保险核算,尤其是非寿险服务产出核算具有重要的启示意义。其次运用广东省（不含深圳）保险业的数据和期望法对06、07年的总产出进行计算,构建预测模型,并比较分析计算的结果。最后,本文提出对改进我国保险核算的建议。     

Estimation of sex‐specific survival with uncertainty in sex assessment

In monomorphic species, determination of sex from behavior is prone to errors. The authors develop capture‐recapture survival models that account for uncertainty in the assessment of sex. They examine parameter redundancy for four basic models with constant or time‐dependent survival and encounter probabilities. They further develop a more refined and more appropriate model for an Audouin's gull data set where four distinct behavioral clues have been used. They examine how useful it is to incorporate the least reliable of the clues and the genetic determination of sex available for only a handful of individuals. They finally discuss the implications of their findings for the design of field studies.