A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model

1999/05/01 by Harry H. Kelejian, Ingmar R. Prucha · 1,490 citations
Economics, Econometrics and Finance · Mathematics · #Applied mathematics #Autocorrelation #Autoregressive integrated moving average #Autoregressive model #Bias of an estimator #Consistent estimator #Economic and Environmental Valuation #Efficient estimator #Estimation theory #Estimator #Generalized method of moments #Mathematics #Maximum likelihood #Method of moments (probability theory) #Minimax estimator #Minimum-variance unbiased estimator #Regional Economics and Spatial Analysis #STAR model #Spatial and Panel Data Analysis #Statistics

paper · doi:10.1111/1468-2354.00027

published in International Economic Review 40(2), 509-533 (Wiley)

openalex publication_date 1999/05/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31

Abstract

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 this 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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