A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model
Harry H. Kelejian () and
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Harry H. Kelejian: Department of Economics, University of Maryland, http://econweb.umd.edu/~kelejian/
Electronic Working Papers from University of Maryland, Department of Economics
This 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.
Keywords: Spatial Models; Autocorrelation; Generalized Moments Estimator; Feasible GLS (search for similar items in EconPapers)
JEL-codes: C13 C21 (search for similar items in EconPapers)
Date: 1995-02, Revised 1997-03
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Journal Article: A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model (1999)
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Persistent link: https://EconPapers.repec.org/RePEc:umd:umdeco:95-001
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Department of Economics, University of Maryland, Tydings Hall, College Park, MD 20742
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