Grivas, C. and Psaradakis, Zacharias (2025) Automated bandwidth selection for inference in linear models with time-varying coefficients. Journal of Time Series Analysis , ISSN 0143-9782.
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Abstract
The problem of selecting the smoothing parameter, or bandwidth, for kernel-based estimators of time-varying coefficients in linear models with possibly endogenous explanatory variables is considered. We examine automated bandwidth selection by means of cross-validation, a nonparametric variant of Akaike's information criterion, and bootstrap procedures based on wild bootstrap and dependent wild bootstrap resampling schemes. Our simulations show that data-driven selectors based on cross-validation and the dependent wild bootstrap are the most successful overall in a variety of settings that are relevant in econometrics. Empirical examples illustrate the practical use of the automated procedures.
Metadata
| Item Type: | Article |
|---|---|
| Keyword(s) / Subject(s): | Data-driven bandwidth, Instrumental variables, Kernel smoothing, Time-varying coefficients |
| School: | Birkbeck Faculties and Schools > Faculty of Business and Law > Birkbeck Business School |
| Depositing User: | Zacharias Psaradakis |
| Date Deposited: | 23 Apr 2025 13:31 |
| Last Modified: | 19 Sep 2025 20:02 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/55442 |
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