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Causal Inference in Econometrics pp 17–28Cite as

Rapid Optimal Lag Order Detection and Parameter Estimation of Standard Long Memory Time Series

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 622))

Abstract

Objective of this paper is to highlight the rapid assessment (in a few minutes) of fractionally differenced standard long memory time series in terms of parameter estimation and optimal lag order assessment. Initially, theoretical aspects of standard fractionally differenced processes with long memory and related state space modelling will be discussed. An efficient mechanism based on theory to estimate parameters and detect optimal lag order in minimizing processing speed and turnaround time is introduced subsequently. The methodology is extended using an available result in literature to present rapid results of an optimal fractionally differenced standard long memory model. Finally, the technique is applied to a couple of real data applications illustrating it’s feasibility and importance.

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Authors and Affiliations

  1. School of Mathematics and Statistics, University of Sydney, Sydney, NSW, Australia

    G. S. Dissanayake

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  1. G. S. Dissanayake
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Correspondence to G. S. Dissanayake .

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Editors and Affiliations

  1. School of Knowledge Science, Japan Advanced Ins. of Sci. & Tech, Ishikawa, Japan

    Van-Nam Huynh

  2. Department of Computer Science, University of Texas at El Paso, El Paso, Texas, USA

    Vladik Kreinovich

  3. Faculty of Economics, Chiang Mai University, Chiangmai, Thailand

    Songsak Sriboonchitta

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Dissanayake, G.S. (2016). Rapid Optimal Lag Order Detection and Parameter Estimation of Standard Long Memory Time Series. In: Huynh, VN., Kreinovich, V., Sriboonchitta, S. (eds) Causal Inference in Econometrics. Studies in Computational Intelligence, vol 622. Springer, Cham. https://doi.org/10.1007/978-3-319-27284-9_2

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  • DOI: https://doi.org/10.1007/978-3-319-27284-9_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27283-2

  • Online ISBN: 978-3-319-27284-9

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