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Memory Time Series (memory + time_series)

Distribution by Scientific Domains
Mathematics and Statistics100%

Kinds of Memory Time Series

  • long memory time series
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    Selected Abstracts

    Bootstrap-based bandwidth choice for log-periodogram regression

    JOURNAL OF TIME SERIES ANALYSIS, Issue 6 2009
    Josu Arteche
    Abstract., The choice of the bandwidth in the local log-periodogram regression is of crucial importance for estimation of the memory parameter of a long memory time series. Different choices may give rise to completely different estimates, which may lead to contradictory conclusions, for example about the stationarity of the series. We propose here a data-driven bandwidth selection strategy that is based on minimizing a bootstrap approximation of the mean-squared error (MSE). Its behaviour is compared with other existing techniques for optimal bandwidth selection in a MSE sense, revealing its better performance in a wider class of models. The empirical applicability of the proposed strategy is shown with two examples: the widely analysed in a long memory context Nile river annual minimum levels and the input gas rate series of Box and Jenkins. [source]

    Averaged Periodogram Spectral Estimation with Long-memory Conditional Heteroscedasticity

    JOURNAL OF TIME SERIES ANALYSIS, Issue 4 2001
    Marc Henry
    The empirical relevance of long-memory conditional heteroscedasticity has emerged in a variety of studies of long time series of high frequency financial measurements. A reassessment of the applicability of existing semiparametric frequency domain tools for the analysis of time dependence and long-run behaviour of time series is therefore warranted. To that end, in this paper the averaged periodogram statistic is analysed in the framework of a generalized linear process with long-memory conditional heteroscedastic innovations according to a model specification first proposed by Robinson (Testing for strong serial correlation and dynamic conditional heteroscedasticity in multiple regression. J. Economet. 47 (1991), 67,84). It is shown that the averaged periodogram estimate of the spectral density of a short-memory process remains asymptotically normal with unchanged asymptotic variance under mild moment conditions, and that for strongly dependent processes Robinson's averaged periodogram estimate of long memory (Semiparametric analysis of long memory time series. Ann. Stat. 22 (1994), 515,39) remains consistent. [source]

    Inducing normality from non-Gaussian long memory time series and its application to stock return data

    APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, Issue 4 2010
    Kyungduk Ko
    Abstract Motivated by Lee and Ko (Appl. Stochastic Models. Bus. Ind. 2007; 23:493,502) but not limited to the study, this paper proposes a wavelet-based Bayesian power transformation procedure through the well-known Box,Cox transformation to induce normality from non-Gaussian long memory processes. We consider power transformations of non-Gaussian long memory time series under the assumption of an unknown transformation parameter, a situation that arises commonly in practice, while most research has been devoted to non-linear transformations of Gaussian long memory time series with known transformation parameter. Specially, this study is mainly focused on the simultaneous estimation of the transformation parameter and long memory parameter. To this end, posterior estimations via Markov chain Monte Carlo methods are performed in the wavelet domain. Performances are assessed on a simulation study and a German stock return data set. Copyright © 2009 John Wiley & Sons, Ltd. [source]