Publication Type
Journal Article
Version
submittedVersion
Publication Date
11-2011
Abstract
In this paper we introduce a linear programming estimator (LPE) for the slope parameter in a constrained linear regression model with a single regressor. The LPE is interesting because it can be superconsistent in the presence of an endogenous regressor and, hence, preferable to the ordinary least squares estimator (LSE). Two different cases are considered as we investigate the statistical properties of the LPE. In the first case, the regressor is assumed to be fixed in repeated samples. In the second, the regressor is stochastic and potentially endogenous. For both cases the strong consistency and exact finite-sample distribution of the LPE is established. Conditions under which the LPE is consistent in the presence of serially correlated, heteroskedastic errors are also given. Finally, we describe how the LPE can be extended to the case with multiple regressors and conjecture that the extended estimator is consistent under conditions analogous to the ones given herein. Finite-sample properties of the LPE and extended LPE in comparison to the LSE and instrumental variable estimator (IVE) are investigated in a simulation study. One advantage of the LPE is that it does not require an instrument.
Discipline
Econometrics
Research Areas
Econometrics
Publication
Journal of Econometrics
Volume
165
Issue
1
First Page
128
Last Page
136
ISSN
0304-4076
Identifier
10.1016/j.jeconom.2011.05.011
Publisher
Elsevier: 24 months
Citation
PREVE, Daniel P. A. and MEDEIROS, Marcelo C..
Linear programming-based estimators in simple linear regression. (2011). Journal of Econometrics. 165, (1), 128-136.
Available at: https://ink.library.smu.edu.sg/soe_research/2332
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/10.1016/j.jeconom.2011.05.011