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Simultaneous inference for time-varying models
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DOI：10.1016/j.jeconom.2021.03.002.png)
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Abstract
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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. (C) 2021 Elsevier B.V. All rights reserved.
Keywords:
Time-varying regression
Time-series models
Generalized linear models
Simultaneous confidence band
Gaussian approximation
Bootstrap
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