Year: 2023
Type: article
Abstract: In the classic measurement error framework, covariates are contaminated by independent additive noise.This paper considers parameter estimation in such a linear errors-in-variables model where the unknown measurement error distribution is heteroscedastic across observations.We propose a new generalized method of moment (GMM) estimator that combines a moment correction approach and a phase functionbased approach.The former requires distributions to have four finite moments, while the latter relies on covariates having asymmetric distributions.The new estimator is shown to be consistent and asymptotically normal under appropriate regularity conditions.The asymptotic covariance of the estimator is derived, and the estimated standard error is computed using a fast bootstrap procedure.The GMM estimator is demonstrated to have strong finite sample performance in numerical studies, especially when the measurement errors follow non-Gaussian distributions.
Source: Statistica Sinica
Authors: Linh H. Nghiem, Cornelis J. Potgieter
Language: English
Subfield: Applied Mathematics
Field: Mathematics
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4388745947