Simultaneous inference for time-varying models

Sayar Karmakar, Stefan Richter and Wei Biao Wu

Journal of Econometrics, 2022, vol. 227, issue 2, 408-428

Abstract: A general class of non-stationary time series is considered in this paper. We estimate the time-varying coefficients by using local linear M-estimation. For these estimators, weak Bahadur representations are obtained and are used to construct simultaneous confidence bands. For practical implementation, we propose a bootstrap based method to circumvent the slow logarithmic convergence of the theoretical simultaneous bands. Our results substantially generalize and unify the treatments for several time-varying regression and auto-regression models. The performance for tvARCH and tvGARCH models is studied in simulations and a few real-life applications of our study are presented through the analysis of some popular financial datasets.

Keywords: Time-varying regression; Time-series models; Generalized linear models; Simultaneous confidence band; Gaussian approximation; Bootstrap (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:227:y:2022:i:2:p:408-428

DOI: 10.1016/j.jeconom.2021.03.002

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