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A simple nonparametric conditional quantile estimator for time series with thin tails

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  • Wang, Qiao

Abstract

In this study, we consider a simple conditional quantile estimator in a nonparametric framework with time series data. We prove the consistency and asymptotic normality of our simple estimator for absolutely regular processes (β-mixing). This simple estimator can get better finite sample performances at thin tails than the check-function-based estimator. Finite sample simulation results show that our simple estimators have better finite sample performances at thin tails of time series data.

Suggested Citation

  • Wang, Qiao, 2023. "A simple nonparametric conditional quantile estimator for time series with thin tails," Economics Letters, Elsevier, vol. 232(C).
    • Handle: RePEc:eee:ecolet:v:232:y:2023:i:c:s0165176523003749
    DOI: 10.1016/j.econlet.2023.111349
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    References listed on IDEAS

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