PreprintCC BY 4.0arXiv
Second-Order Least Squares Method for Statistical Inference in Linear Models with Asymmetric Errors
Abstract
In a linear regression model with normal random errors, the ordinary least squares estimator is the most efficient estimator. However, when the random errors have an asymmetric distribution, a more efficient estimator can be constructed using the higher moments of the data. In this paper, we use the second-order least squares method to obtain more accurate asymptotic confidence intervals and more powerful hypothesis tests in linear models. We also develop an efficient and stable numerical algorithm to calculate the proposed estimator. Our extensive simulation studies demonstrate the advantages of the proposed method. Two real data examples are presented.
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BibTeX
@misc{kil2026secondorder,
title = {{Second-Order Least Squares Method for Statistical Inference in Linear Models with Asymmetric Errors}},
author = {Sanha Kil and Yuliya V. Martsynyuk and Liqun Wang},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.01017},
eprint = {2611.01017},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.01017},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Second-Order Least Squares Method for Statistical Inference in Linear Models with Asymmetric Errors
AU - Sanha Kil
AU - Yuliya V. Martsynyuk
AU - Liqun Wang
PY - 2026
DA - 2026/11/01
PB - arXiv
DO - 10.48550/arxiv.2611.01017
UR - https://arxiv.org/abs/2611.01017
AB - In a linear regression model with normal random errors, the ordinary least squares estimator is the most efficient estimator. However, when the random errors have an asymmetric distribution, a more efficient estimator can be constructed using the higher moments of the data. In this paper, we use the second-order least squares method to obtain more accurate asymptotic confidence intervals and more powerful hypothesis tests in linear models. We also develop an efficient and stable numerical algorithm to calculate the proposed estimator. Our extensive simulation studies demonstrate the advantages of the proposed method. Two real data examples are presented.
ER -