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

Badi H. Baltagi, Chihwa Kao, Long Liu

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)241-270
Number of pages30
JournalSpatial Economic Analysis
Volume8
Issue number3
DOIs
StatePublished - Sep 2013

Keywords

  • generalized least squares
  • maximum likelihood estimation
  • ordinary least squares
  • spatial autocorrelation
  • two-stage least squares

ASJC Scopus subject areas

  • Geography, Planning and Development
  • General Economics, Econometrics and Finance
  • Statistics, Probability and Uncertainty
  • Earth and Planetary Sciences (miscellaneous)

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