摘要:

Abstract.Outliers in time series seriously affect conventional parameter estimates. In this paper a robust recursive estimation procedure for the parameters of auto‐regressve moving‐average models with additive outliers is proposed. Using ‘cleaned’ residuals from an initial robust fit of an autoregression of high order as input, bounded influence regression is applied recursively. The proposal follows certain ideas of Hannan and Rissanen, who suggested a three‐stage procedure for order and parameter estimation in a conventional setting.A Monte Carlo study is performed to investigate the robustness properties of the proposed class of estimates and to compare them with various other suggestions, including least squares, M estimates, residual autocovariance and truncated residual autocovariance estimates. The results show that the recursive generalized M estimates compare favourably with them. Finally, possible modifications to master even vigourous situations are

ISSN:0143-9782    

DOI:10.1111/j.1467-9892.1992.tb00091.x

出版商:Blackwell Publishing Ltd    

年代:1992

数据来源: WILEY

摘要:

Abstract.A nonparametric test statistic based on the distance between the joint and marginal densities is developed to test for the serial dependence for a given sequence of time series data. The key idea lies in observing that, under the null hypothesis of independence, the joint density of the observations is equal to the product of their individual marginals. Histograms are used in constructing such a statistic which is nonparametric and consistent. It possesses high power in capturing subtle or diffuse dependence structure. A bilinear time series model is used to illustrate its performance with the classical correlation approach.

ISSN:0143-9782    

DOI:10.1111/j.1467-9892.1992.tb00092.x

出版商:Blackwell Publishing Ltd    

年代:1992

数据来源: WILEY

摘要:

Abstract.In this paper we propose test statistics for the null hypothesis of a random walk or a random walk with drift for the case in which the innovations to the series are a moving‐average process. The statistics are based on the instrumental variable estimators proposed by Hall and by Pantula and Hall and are shown to have the limiting distributions tabulated by Dickey and Fulle

ISSN:0143-9782    

DOI:10.1111/j.1467-9892.1992.tb00093.x

出版商:Blackwell Publishing Ltd    

年代:1992

数据来源: WILEY

摘要:

Abstract.In a time series regression model the residual autoregression function is an unknown, possibly non‐linear, function. It is estimated by non‐parametric kernel regression. The resulting least‐squares estimate of the regression function is shown to be adapative, in the sense of having the same asymptotic distribution, to first order, as estimates based on knowledge of the autoregression function. Also, a Monte Carlo experiment about the behaviour of the estimator is desc

ISSN:0143-9782    

DOI:10.1111/j.1467-9892.1992.tb00094.x

出版商:Blackwell Publishing Ltd    

年代:1992

数据来源: WILEY

摘要:

Abstract.Recent contributions by Tong and others in modelling time series exhibiting threshold points have generally been based on approximating non‐linear processes by piecewise linear time series models. In this paper we provide an alternative framework in which to model time series displaying jump behaviour by using a multimodal conditional distribution to capture the jump process. Each subordinate model of the distribution is determined by an autoregressive process, and jump behaviour occurs when the relative heights of the modes of the distribution change whilst the threshold points are identified by the antimodes of the distribution. This class of models is referred to as multipredictor autoregressive time series (MATS

ISSN:0143-9782    

DOI:10.1111/j.1467-9892.1992.tb00095.x

出版商:Blackwell Publishing Ltd    

年代:1992

数据来源: WILEY