Testing coeffcients of AR and bilinear time series models by a graphical approach

AR and bilinear time series models are expressed as time series chain graphical models, based on which, it is shown that the coefficients of AR and bilinear models are the conditional correlation coefficients conditioned on the other components of the time series. Then a graphically based procedure is proposed to test the significance of the coeffcients of AR and bilinear time series. Simulations show that our procedure performs well both in sizes and powers.     

A note on bilinear time series models

Conditions for the existence of a stationary solution for certain forms of bilinear difference equations are derived.     

Stationarity and invertibility of simple bilinear models

Necessary and sufficient conditions for strict stationarity and invertibility are found for one-parameter bilinear models. These conditions involve the expectations of the logarithms of the absolute values of the input and output sequences.     

Modelling lifetime data: a graphical approach

In this paper we point out the differences between the most common hazard-based models, such as the proportional hazards and the accelerated failure time models. We focus on the heteroscestaticity-across-individuals problem that cannot be accommodated by them, and give motivation and general ideas about more flexible formulations. We describe hybrid and extended models, which have the former models as particular cases, but keep enough flexibility to fit data with heteroscedasticity. We show that by considering simple graphical procedures it is easy to verify whether there is heteroscedasticity in the data, whether it is possible to describe it through a simple function of the covariates, and whether it is important to take it in account for the final fit. Real datasets are considered. Copyright © 1999 John Wiley & Sons, Ltd.     

Tail dependence of random variables from ARCH and heavy-tailed bilinear models

Discussed in this paper is the dependent structure in the tails of distributions of random variables from some heavy-tailed stationary nonlinear time series. One class of models discussed is the first-order autoregressive conditional heteroscedastic (ARCH) process introduced by Engle (1982). The other class is the simple first-order bilinear models driven by heavy-tailed innovations. We give some explicit formulas for the asymptotic values of conditional probabilities used for measuring the tail dependence between two random variables from these models. Our results have significant meanings in finance.     

On the invertibility of time series models

A generalized definition of invertibility is proposed and applied to linear, non-linear and bilinear models. It is shown that some recently studied non-linear models are not invertible, but conditions for invertibility can be achieved for the other models.     

Estimation of the parameters for unstable AR models

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Bilinear markovian representation and bilinear models

An extension of the linear Markovian repsentation called the bilinear Markovian representation is introduced, and is shown to provide representations of all-diagonal bilinear time series models. Some properties of the bilinear Markovian representation are also given.     

Feedback stabilization of non-homogeneous bilinear systems with a finite time delay

This paper investigates the feedback stabilization of non-homogeneous delayed bilinear systems in Hilbert state space. More precisely, under an observability-like assumption, we prove the exponential and strong stability of the solution by using bounded feedback control. Partial stabilization is discussed as well. The proofs of the main results are based on the decomposition method. The decay estimates of the stabilized solutions are obtained. Finally, some examples are presented.     

A bilinear approach to the restriction and Kakeya conjectures

Bilinear restriction estimates have appeared in work of Bourgain, Klainerman, and Machedon. In this paper we develop the theory of these estimates (together with the analogues for Kakeya estimates). As a consequence we improve the spherical restriction theorem of Wolff from to , and also obtain a sharp spherical restriction theorem for .

     

Testing nonstationary and absolutely regular nonlinear time series models

Statistical Inference for Stochastic Processes - We study some general methods for testing the goodness-of-fit of a general nonstationary and absolutely regular nonlinear time series model. These...     

Risk models based on time series for count random variables

In this paper, we generalize the classical discrete time risk model by introducing a dependence relationship in time between the claim frequencies. The models used are the Poisson autoregressive model and the Poisson moving average model. In particular, the aggregate claim amount and related quantities such as the stop-loss premium, value at risk and tail value at risk are discussed within this framework.     

A new algorithm for decomposition of graphical models

In this paper, we combine Leimer’s algorithm with MCS-M algorithm to decompose graphical models into marginal models on prime blocks. It is shown by experiments that our method has an easier and faster implementation than Leimer’s algorithm.     

A graphical method for a class of Branin trajectories

The construction of Branin trajectories for locating the stationary points of a scalar function of many variables involves, in the general case, the numerical solution of a set of simultaneous ordinary differential equations, or some equivalent numerical procedure. For a function of only two variables which is separable in either the multiplicative or additive sense, it is shown that Branin trajectories may be obtained by a graphical method due to Volterra.The authors are indebted to Dr. L. C. W. Dixon for his helpful comments on the original draft of this paper.     

图形化模型在亲子鉴定中的应用

利用图形化模型理论,以及DNA数据,研究了一个在法庭上颇有争议的亲子鉴定问题.通过案例中的家谱图,建立Bayesian网络,根据遗传学的孟德尔定律,借助于Hug in软件,计算出网络中各结点的概率.在数学上,给出了一个可供法庭参考的合理推断.     

Estimation of the arrival times of seismic waves by multivariate time series model

A computationally efficient procedure was developed for the fitting of many multivariate locally stationary autoregressive models. The details of the Householder method for fitting multivariate autoregressive model and multivariate locally stationary autoregressive model (MLSAR model) are shown. The proposed procedure is quite efficient in both accuracy and computation. The amount of computation is bounded by a multiple of Nm ² with N being the data length and m the highest model order, and does not depend on the number of models checked. This facilitates the precise estimation of the change point of the AR model. Based on the AICs' of the fitted MLSAR models and Akaike's definition of the likelihood of the models, a method of evaluating the posterior distribution of the change point of the AR model is also presented. The proposed procedure is, in particular, useful for the estimation of the arrival time of the S wave of a microearthquake. To illustrate the usefulness of the proposed procedure, the seismograms of the foreshocks of the 1982 Urakawa-Oki Earthquake were analyzed. These data sets have been registered to AISM Data Library and the readers of this Journal can access to them by the method described in this issue.A part of this research was carried out under the ISM Cooperative Research Program (89-ISM.CRP-57).Also with the Faculty of Economics, the University of Tokyo. The author was supported in part by the Japanese Ministry of Education, Science and Culture under Grant-in-Aid for Developmental Scientific Research 63830002.     

Testing for serial correlation of unknown form in cointegrated time series models

Portmanteau test statistics are useful for checking the adequacy of many time series models. Here we generalized the omnibus procedure proposed by Duchesne and Roy (2004,Journal of Multivariate Analysis,89, 148–180) for multivariate stationary autoregressive models with exogenous variables (VARX) to the case of cointegrated (or partially nonstationary) VARX models. We show that for cointegrated VARX time series, the test statistic obtained by comparing the spectral density of the errors under the null hypothesis of non-correlation with a kernel-based spectral density estimator, is asymptotically standard normal. The parameters of the model can be estimated by conditional maximum likelihood or by asymptotically equivalent estimation procedures. The procedure relies on a truncation point or a smoothing parameter. We state conditions under which the asymptotic distribution of the test statistic is unaffected by a data-dependent method. The finite sample properties of the test statistics are studied via a small simulation study.     

Jump detection in time series nonparametric regression models: a polynomial spline approach

For time series nonparametric regression models with discontinuities, we propose to use polynomial splines to estimate locations and sizes of jumps in the mean function. Under reasonable conditions, test statistics for the existence of jumps are given and their limiting distributions are derived under the null hypothesis that the mean function is smooth. Simulations are provided to check the powers of the tests. A climate data application and an application to the US unemployment rates of men and women are used to illustrate the performance of the proposed method in practice.     

Causality analysis of futures sugar prices in Zhengzhou based on graphical models for multivariate time series

This paper presents a method of constructing a mixed graph which can be used to analyze the causality for multivariate time series. We construct a partial correlation graph at first which is an undirected graph. For every undirected edge in the partial correlation graph, the measures of linear feedback between two time series can help us decide its direction, then we obtain the mixed graph. Using this method, we construct a mixed graph for futures sugar prices in Zhengzhou (ZF), spot sugar prices in Zhengzhou (ZS) and futures sugar prices in New York (NF). The result shows that there is a bi-directional causality between ZF and ZS, an unidirectional causality from NF to ZF, but no causality between NF and ZS.     

Analyzing short time series data from periodically fluctuating rodent populations by threshold models: A nearest block bootstrap approach

The study of the rodent fluctuations of the North was initiated in its modern form with Elton’s pioneering work. Many scientific studies have been designed to collect yearly rodent abundance data, but the resulting time series are generally subject to at least two “problems”: being short and non-linear. We explore the use of the continuous threshold autoregressive (TAR) models for analyzing such data. In the simplest case, the continuous TAR models are additive autoregressive models, being piecewise linear in one lag, and linear in all other lags. The location of the slope change is called the threshold parameter. The continuous TAR models for rodent abundance data can be derived from a general prey-predator model under some simplifying assumptions. The lag in which the threshold is located sheds important insights on the structure of the prey-predator system. We propose to assess the uncertainty on the location of the threshold via a new bootstrap called the nearest block bootstrap (NBB) which combines the methods of moving block bootstrap and the nearest neighbor bootstrap. The NBB assumes an underlying finite-order time-homogeneous Markov process. Essentially, the NBB bootstraps blocks of random block sizes, with each block being drawn from a non-parametric estimate of the future distribution given the realized past bootstrap series. We illustrate the methods by simulations and on a particular rodent abundance time series from Kilpisjärvi, Northern Finland.