A multi-step kernel–based regression estimator that adapts to error distributions of unknown form

De Gooijer, Jan G. and Reichardt, Hugo (2021) A multi-step kernel–based regression estimator that adapts to error distributions of unknown form. Communications in Statistics - Theory and Methods, 50 (24). 6211 - 6230. ISSN 0361-0926

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Abstract

For linear regression models, we propose and study a multi-step kernel density-based estimator that is adaptive to unknown error distributions. We establish asymptotic normality and almost sure convergence. An efficient EM algorithm is provided to implement the proposed estimator. We also compare its finite sample performance with five other adaptive estimators in an extensive Monte Carlo study of eight error distributions. Our method generally attains high mean-square-error efficiency. An empirical example illustrates the gain in efficiency of the new adaptive method when making statistical inference about the slope parameters in three linear regressions.

Item Type:	Article
Official URL:	https://www.tandfonline.com/journals/lsta20
Additional Information:	© 2020 The Authors
Divisions:	Centre for Macroeconomics
Subjects:	H Social Sciences > HB Economic Theory Q Science > QA Mathematics
Date Deposited:	11 May 2022 13:36
Last Modified:	09 Nov 2024 18:06
URI:	http://eprints.lse.ac.uk/id/eprint/115083

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