GLS under monotone heteroskedasticity

Arai, Yoichi, Otsu, Taisuke ORCID: 0000-0002-2307-143X and Xu, Mengshan (2024) GLS under monotone heteroskedasticity. Journal of Econometrics, 246 (1-2). ISSN 0304-4076

Text (1-s2.0-S0304407624002501-main) - Published Version Available under License Creative Commons Attribution. Download (781kB)

• Scopus publication

Abstract

The generalized least square (GLS) is one of the most basic tools in regression analyses. A major issue in implementing the GLS is estimation of the conditional variance function of the error term, which typically requires a restrictive functional form assumption for parametric estimation or smoothing parameters for nonparametric estimation. In this paper, we propose an alternative approach to estimate the conditional variance function under nonparametric monotonicity constraints by utilizing the isotonic regression method. Our GLS estimator is shown to be asymptotically equivalent to the infeasible GLS estimator with knowledge of the conditional error variance, and involves only some tuning to trim boundary observations, not only for point estimation but also for interval estimation or hypothesis testing. Simulation studies and an empirical example illustrate excellent finite sample performances of the proposed method.

Item Type:	Article
Additional Information:	© 2024 The Author(s)
Divisions:	Economics
Subjects:	H Social Sciences > HB Economic Theory
JEL classification:	C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods: General > C13 - Estimation C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods: General > C14 - Semiparametric and Nonparametric Methods
Date Deposited:	04 Nov 2024 16:45
Last Modified:	12 Dec 2024 04:33
URI:	http://eprints.lse.ac.uk/id/eprint/125941

Actions (login required)

View Item