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Stochastic equicontinuity in nonlinear time series models

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  • Andreas Hagemann

Abstract

In this paper, I provide simple and easily verifiable conditions under which a strong form of stochastic equicontinuity holds in a wide variety of modern time series models. In contrast to most results currently available in the literature, my methods avoid mixing conditions. I discuss several applications in detail.

Suggested Citation

  • Andreas Hagemann, 2014. "Stochastic equicontinuity in nonlinear time series models," Econometrics Journal, Royal Economic Society, vol. 17(1), pages 188-196, February.
    • Handle: RePEc:wly:emjrnl:v:17:y:2014:i:1:p:188-196
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    File URL: http://hdl.handle.net/10.1111/ectj.12013
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    Cited by:

    1. Christis Katsouris, 2023. "High Dimensional Time Series Regression Models: Applications to Statistical Learning Methods," Papers 2308.16192, arXiv.org.
    2. Antoine Djogbenou & Christian Gouriéroux & Joann Jasiak & Paul Rilstone, 2022. "An Econometric Panel Data Model of the COVID-19 Pandemic," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 11(1), pages 1-3.
    3. Yinxiao Huang & Stanislav Volgushev & Xiaofeng Shao, 2015. "On Self-Normalization For Censored Dependent Data," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(1), pages 109-124, January.

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