Interpretation and Computation of Estimates from Regression Models using Spatial Filtering

Document Type

Article

Publication Date

9-1-2013

Abstract

Spatial filtering in various forms has become a popular way to address spatial dependence in statistical models (Griffith, 2003; Tiefelsdorf & Griffith, 2007). However, spatial filtering faces computational challenges for large n as the current method requires order of n3 operations. This manuscript demonstrates how using iterative eigenvalue routines on sparse weight matrices can make filtering feasible for data sets involving a million or more observations and empirically estimates an operation count on the order of n1.1. Moreover, we show that filtering performs better, both statistically and numerically, for spatial weight matrices with more neighbours. Finally, we show that although filtering out spatial aspects of the data reduces bias in parameter estimates for the spatially lagged dependent variable DGP, it also filters out spatial aspects of interest such as spillovers. © 2013 Copyright Regional Studies Association.

Publication Source (Journal or Book title)

Spatial Economic Analysis

First Page

352

Last Page

369

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