A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model
AbstractThis paper is concerned with the estimation of the autoregressive parameter in a widely considered spatial autocorrelation model. The typical estimator for this parameter considered in the literature is the (quasi) maximum likelihood estimator corresponding to a normal density. However, as discussed in the paper, the (quasi) maximum likelihood estimator may not be computationally feasible in many cases involving moderate or large sized samples. In this paper we suggest a generalized moments estimator that is computationally simple irrespective of the sample size. We provide results concerning the large and small sample properties of this estimator.
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Bibliographic InfoPaper provided by University of Maryland, Department of Economics in its series Electronic Working Papers with number 95-001.
Date of creation: Feb 1995
Date of revision: Mar 1997
Contact details of provider:
Postal: Department of Economics, University of Maryland, Tydings Hall, College Park, MD 20742
Web page: http://www.econ.umd.edu/
Postal: Ms. Elizabeth Martinez, Department of Economics, University of Maryland, Tydings Hall, College Park, MD 20742
Find related papers by JEL classification:
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
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