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Stochastic Models, Statistics and Their Applications pp 207–214Cite as

Quasi-maximum Likelihood Estimation of Periodic Autoregressive, Conditionally Heteroscedastic Time Series

Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS,volume 122)

Abstract

We consider a general multivariate periodically stationary and ergodic causal time series model. We prove consistency and asymptotic normality of the quasi-maximum likelihood (QML) estimator of it. Applications to the multivariate nonlinear periodic AR(∞)–ARCH(∞) process are shown.

Keywords

  • Quasi-maximum Likelihood (QML)
  • Periodic Autoregressive
  • QML Estimation
  • Time Series Models
  • Asymptotic Normality

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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Author information

Authors and Affiliations

  1. Europa-Universität Viadrina, Frankfurt (Oder), Germany

    Florian Ziel

Authors
  1. Florian Ziel
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Corresponding author

Correspondence to Florian Ziel .

Editor information

Editors and Affiliations

  1. Institute of Statistics, RWTH Aachen University, Aachen, Germany

    Ansgar Steland

  2. Dept. of Computer Engineering, Control and Robotics, Wrocław University of Technology, Wrocław, Poland

    Ewaryst Rafajłowicz

  3. Inst. of Mathematics and Computer Science, Wrocław University of Technology, Wrocław, Poland

    Krzysztof Szajowski

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Cite this paper

Ziel, F. (2015). Quasi-maximum Likelihood Estimation of Periodic Autoregressive, Conditionally Heteroscedastic Time Series. In: Steland, A., Rafajłowicz, E., Szajowski, K. (eds) Stochastic Models, Statistics and Their Applications. Springer Proceedings in Mathematics & Statistics, vol 122. Springer, Cham. https://doi.org/10.1007/978-3-319-13881-7_23

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