Publication Type
Journal Article
Version
acceptedVersion
Publication Date
9-2015
Abstract
In this paper we consider certain measure of location-based estimators (MLBEs) for the slope parameter in a linear regression model with a single stochastic regressor. The median-unbiased MLBEs are interesting as they can be robust to heavy-tailed samples and, hence, preferable to the ordinary least squares estimator (LSE). Two different cases are considered as we investigate the statistical properties of the MLBEs. In the first case, the regressor and error are assumed to follow a symmetric stable distribution. In the second, other types of regressions, with potentially contaminated errors, are considered. For both cases the consistency and exact finite-sample distributions of the MLBEs are established. Some results for the corresponding limiting distributions are also provided. In addition, we illustrate how our results can be extended to include certain heteroscedastic regressions. Finite-sample properties of the MLBEs in comparison to the LSE are investigated in a simulation study.
Discipline
Econometrics
Research Areas
Econometrics
Publication
Journal of Statistical Computation and Simulation
Volume
86
Issue
9
First Page
1
Last Page
14
ISSN
0094-9655
Identifier
10.1080/00949655.2015.1082131
Publisher
Taylor & Francis: STM, Behavioural Science and Public Health Titles
Citation
LIU, Xijia and PREVE, Daniel P. A..
Measure of location-based estimators in simple linear regression. (2015). Journal of Statistical Computation and Simulation. 86, (9), 1-14.
Available at: https://ink.library.smu.edu.sg/soe_research/2329
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1080/00949655.2015.1082131