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.
![]() |
Text
bandwidthchoice-JTSA-final.pdf - Author's Accepted Manuscript Restricted to Repository staff only Available under License Creative Commons Attribution. Download (983kB) | Request a copy |
![]() |
Text
55442a.pdf - Published Version of Record Available under License Creative Commons Attribution. Download (3MB) |
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: | 01 Sep 2025 18:48 |
URI: | https://eprints.bbk.ac.uk/id/eprint/55442 |
Statistics
Additional statistics are available via IRStats2.