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The Estimation and Testing of a Linear Regression with Near Unit Root in the Spatial Autoregressive Error Term

Badi Baltagi (), Chihwa Kao () and Long Liu
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Long Liu: Department of Economics, College of Business, University of Texas at San Antonio

No 150, Center for Policy Research Working Papers from Center for Policy Research, Maxwell School, Syracuse University

Abstract: This paper considers the estimation of a linear regression involving the spatial autoregressive (SAR) error term, which is nearly nonstationary. The asymptotics properties of the ordinary least squares (OLS), true generalized least squares (GLS) and feasible generalized least squares (FGLS) estimators as well as the corresponding Wald test statistics are derived. Monte Carlo results are conducted to study the sampling behavior of the proposed estimators and test statistics. Key Words: Spatial Autocorrelation; Ordinary Least Squares; Generalized Least Squares; Two-stage Least Squares; Maximum Likelihood Estimation JEL No. C23, C33

New Economics Papers: this item is included in nep-ecm, nep-ets and nep-geo
Date: 2012-12
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Journal Article: The Estimation and Testing of a Linear Regression with Near Unit Root in the Spatial Autoregressive Error Term (2013) Downloads
Working Paper: The Estimation and Testing of a Linear Regression with Near Unit Root in the Spatial Autoregressive Error Term (2012)
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