Linear programming-based estimators in simple linear regression

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 s...

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Main Authors: PREVE, Daniel P. A., MEDEIROS, Marcelo C.
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Language:English
Published: Institutional Knowledge at Singapore Management University 2011
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Online Access:https://ink.library.smu.edu.sg/soe_research/2332
https://ink.library.smu.edu.sg/context/soe_research/article/3331/viewcontent/Linear_Programming_Based_Estimators_in_S.pdf
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spelling sg-smu-ink.soe_research-33312020-01-09T06:22:09Z Linear programming-based estimators in simple linear regression PREVE, Daniel P. A. MEDEIROS, Marcelo C. 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. 2011-11-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/soe_research/2332 info:doi/10.1016/j.jeconom.2011.05.011 https://ink.library.smu.edu.sg/context/soe_research/article/3331/viewcontent/Linear_Programming_Based_Estimators_in_S.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University Econometrics
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Econometrics
spellingShingle Econometrics
PREVE, Daniel P. A.
MEDEIROS, Marcelo C.
Linear programming-based estimators in simple linear regression
description 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.
format text
author PREVE, Daniel P. A.
MEDEIROS, Marcelo C.
author_facet PREVE, Daniel P. A.
MEDEIROS, Marcelo C.
author_sort PREVE, Daniel P. A.
title Linear programming-based estimators in simple linear regression
title_short Linear programming-based estimators in simple linear regression
title_full Linear programming-based estimators in simple linear regression
title_fullStr Linear programming-based estimators in simple linear regression
title_full_unstemmed Linear programming-based estimators in simple linear regression
title_sort linear programming-based estimators in simple linear regression
publisher Institutional Knowledge at Singapore Management University
publishDate 2011
url https://ink.library.smu.edu.sg/soe_research/2332
https://ink.library.smu.edu.sg/context/soe_research/article/3331/viewcontent/Linear_Programming_Based_Estimators_in_S.pdf
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