Asymptotic efficiency of the OLSE for polynomial regression models with spatially correlated errors

Dong Wan Shin; Seuck Heun Song

doi:10.1016/s0167-7152(99)00092-9

Asymptotic efficiency of the OLSE for polynomial regression models with spatially correlated errors

Dong Wan Shin, Seuck Heun Song

Department of Statistics

Research output: Contribution to journal › Article › peer-review

3 Scopus citations

Abstract

For polynomial regression models with spatially correlated errors, the covariance matrix of the ordinary least-squares estimator (OLSE) is shown to have the same limiting value as that of the generalized least-squares estimator (GLSE) under the same normalization. This implies that the OLSE is asymptotically efficient.

Original language	English
Pages (from-to)	1-10
Number of pages	10
Journal	Statistics and Probability Letters
Volume	47
Issue number	1
DOIs	https://doi.org/10.1016/s0167-7152(99)00092-9
State	Published - 15 Mar 2000

Bibliographical note

Funding Information:
This work was supported by grant No. 1999-1-104-001-5 from the interdisciplinary research program of KOSEF.

Keywords

Efficiency
GLSE
OLSE
Polynomial regression model
Spatial correlation

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10.1016/s0167-7152(99)00092-9

Cite this

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abstract = "For polynomial regression models with spatially correlated errors, the covariance matrix of the ordinary least-squares estimator (OLSE) is shown to have the same limiting value as that of the generalized least-squares estimator (GLSE) under the same normalization. This implies that the OLSE is asymptotically efficient.",

keywords = "Efficiency, GLSE, OLSE, Polynomial regression model, Spatial correlation",

author = "Shin, {Dong Wan} and Song, {Seuck Heun}",

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AU - Song, Seuck Heun

N1 - Funding Information: This work was supported by grant No. 1999-1-104-001-5 from the interdisciplinary research program of KOSEF.

PY - 2000/3/15

Y1 - 2000/3/15

N2 - For polynomial regression models with spatially correlated errors, the covariance matrix of the ordinary least-squares estimator (OLSE) is shown to have the same limiting value as that of the generalized least-squares estimator (GLSE) under the same normalization. This implies that the OLSE is asymptotically efficient.

AB - For polynomial regression models with spatially correlated errors, the covariance matrix of the ordinary least-squares estimator (OLSE) is shown to have the same limiting value as that of the generalized least-squares estimator (GLSE) under the same normalization. This implies that the OLSE is asymptotically efficient.

KW - Efficiency

KW - GLSE

KW - OLSE

KW - Polynomial regression model

KW - Spatial correlation

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Asymptotic efficiency of the OLSE for polynomial regression models with spatially correlated errors

Abstract

Bibliographical note

Keywords

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