Testing for spatial lag and spatial error dependence using double length artificial regressions

Badi H. Baltagi; Long Liu

doi:10.1007/s00362-012-0492-8

Testing for spatial lag and spatial error dependence using double length artificial regressions

Badi H. Baltagi, Long Liu

Department of Economics

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

4 Scopus citations

Abstract

This paper obtains the joint and conditional Lagrange multiplier (LM) tests for a spatial lag regression model with spatial auto-regressive error derived in Anselin (Reg Sci Urban Ecom 26:77-104, 1996) using artificial double length regressions (DLR). These DLR tests and their corresponding LM tests are compared using an illustrative example and a Monte Carlo simulation.

Original language	English (US)
Pages (from-to)	477-486
Number of pages	10
Journal	Statistical Papers
Volume	55
Issue number	2
DOIs	https://doi.org/10.1007/s00362-012-0492-8
State	Published - May 2014

Keywords

Artificial regressions
Double length regression
Spatial error dependence
Spatial lag dependence

ASJC Scopus subject areas

Statistics and Probability
Statistics, Probability and Uncertainty

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10.1007/s00362-012-0492-8

Cite this

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abstract = "This paper obtains the joint and conditional Lagrange multiplier (LM) tests for a spatial lag regression model with spatial auto-regressive error derived in Anselin (Reg Sci Urban Ecom 26:77-104, 1996) using artificial double length regressions (DLR). These DLR tests and their corresponding LM tests are compared using an illustrative example and a Monte Carlo simulation.",

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AB - This paper obtains the joint and conditional Lagrange multiplier (LM) tests for a spatial lag regression model with spatial auto-regressive error derived in Anselin (Reg Sci Urban Ecom 26:77-104, 1996) using artificial double length regressions (DLR). These DLR tests and their corresponding LM tests are compared using an illustrative example and a Monte Carlo simulation.

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KW - Spatial lag dependence

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Testing for spatial lag and spatial error dependence using double length artificial regressions

Abstract

Keywords

ASJC Scopus subject areas

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