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    Automated bandwidth selection for inference in linear models with time-varying coefficients

    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: 01 Sep 2025 18:48
    URI: https://eprints.bbk.ac.uk/id/eprint/55442

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