A1740
Title: Automated bandwidth selection for inference in linear models with time-varying coefficients
Authors: Charisios Grivas - Aalborg University (Denmark) [presenting]
Zacharias Psaradakis - Birkbeck University of London (United Kingdom)
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. Automated bandwidth selection is examined 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. The 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. An empirical example illustrates the practical use of automated procedures.
