Estimation of spatial autoregressive models with measurement error for large data sets

Abstract

Maximum likelihood (ML) estimation of spatial autoregressive models for large spatial data sets is well established by making use of the commonly sparse nature of the contiguity matrix on which spatial dependence is built. Adding a measurement error that naturally separates the spatial process from the measurement error process are not well established in the literature, however, and ML estimation of such models to large data sets is challenging. Recently a reduced rank approach was suggested which re-expresses and approximates such a model as a spatial random effects model (SRE) in order to achieve fast fitting of large data sets by fitting the corresponding SRE. In this paper we propose a fast and exact method to accomplish ML estimation and restricted ML estimation of complexity of $$O(n^{3/2})$$ operations when the contiguity matrix is based on a local neighbourhood. The methods are illustrated using the well known data set on house prices in Lucas County in Ohio.