Simultaneous inference for time-varying models
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 implementati...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article (Journal) |
| Language: | English |
| Published: |
22 February 2022
|
| In: |
Journal of econometrics
Year: 2022, Volume: 227, Issue: 2, Pages: 408-428 |
| DOI: | 10.1016/j.jeconom.2021.03.002 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1016/j.jeconom.2021.03.002 Verlag, lizenzpflichtig, Volltext: https://www.sciencedirect.com/science/article/pii/S0304407621000725 
|
| Author Notes: | Sayar Karmakar, Stefan Richter, Wei Biao Wu |
| Summary: | 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. |
|---|---|
| Item Description: | Gesehen am 15.07.2022 |
| Physical Description: | Online Resource |
| DOI: | 10.1016/j.jeconom.2021.03.002 |