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Adaptive Student's t-distribution with method of moments moving estimator for nonstationary time series

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  • Jarek Duda

Abstract

The real life time series are usually nonstationary, bringing a difficult question of model adaptation. Classical approaches like ARMA-ARCH assume arbitrary type of dependence. To avoid such bias, we will focus on recently proposed agnostic philosophy of moving estimator: in time $t$ finding parameters optimizing e.g. $F_t=\sum_{\tau

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  • Jarek Duda, 2023. "Adaptive Student's t-distribution with method of moments moving estimator for nonstationary time series," Papers 2304.03069, arXiv.org, revised Apr 2023.
    • Handle: RePEc:arx:papers:2304.03069
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    References listed on IDEAS

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    1. Jarek Duda, 2020. "Adaptive exponential power distribution with moving estimator for nonstationary time series," Papers 2003.02149, arXiv.org, revised Mar 2020.
    2. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    3. Hervé Cardot & David Degras, 2018. "Online Principal Component Analysis in High Dimension: Which Algorithm to Choose?," International Statistical Review, International Statistical Institute, vol. 86(1), pages 29-50, April.
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