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Algorithms for Linear Time Series Analysis: With R Package

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  • McLeod, A. Ian
  • Yu, Hao
  • Krougly, Zinovi L.

Abstract

Our ltsa package implements the Durbin-Levinson and Trench algorithms and provides a general approach to the problems of fitting, forecasting and simulating linear time series models as well as fitting regression models with linear time series errors. For computational efficiency both algorithms are implemented in C and interfaced to R. Examples are given which illustrate the efficiency and accuracy of the algorithms. We provide a second package FGN which illustrates the use of the ltsa package with fractional Gaussian noise (FGN). It is hoped that the ltsa will provide a base for further time series software.

Suggested Citation

  • McLeod, A. Ian & Yu, Hao & Krougly, Zinovi L., 2007. "Algorithms for Linear Time Series Analysis: With R Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 23(i05).
    • Handle: RePEc:jss:jstsof:v:023:i05
    DOI: http://hdl.handle.net/10.18637/jss.v023.i05
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    References listed on IDEAS

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    1. Zinde-Walsh, Victoria, 1988. "Some Exact Formulae for Autoregressive Moving Average Processes," Econometric Theory, Cambridge University Press, vol. 4(3), pages 384-402, December.
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    Cited by:

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    2. Derek Nokes & Lawrence Fulton, 2019. "Analysis of a Global Futures Trend-Following Strategy," JRFM, MDPI, vol. 12(3), pages 1-18, June.
    3. Rypdal, Martin & Løvsletten, Ola, 2013. "Modeling electricity spot prices using mean-reverting multifractal processes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(1), pages 194-207.
    4. Ola L{o}vsletten & Martin Rypdal, 2012. "A multifractal approach towards inference in finance," Papers 1202.5376, arXiv.org.
    5. Justin Dang & Aman Ullah, 2023. "Generalized kernel regularized least squares estimator with parametric error covariance," Empirical Economics, Springer, vol. 64(6), pages 3059-3088, June.
    6. Giovanni Masala & Marco Micocci & Andrea Rizk, 2022. "Hedging Wind Power Risk Exposure through Weather Derivatives," Energies, MDPI, vol. 15(4), pages 1-30, February.

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