Small sample properties of maximum likelihood versus generalized method of moments based tests for spatially autocorrelated errors

B-Tier
Journal: Regional Science and Urban Economics
Year: 2009
Volume: 39
Issue: 6
Pages: 670-678

Score contribution per author:

0.503 = (α=2.01 / 4 authors) × 1.0x B-tier

α: calibrated so average coauthorship-adjusted count equals average raw count

Abstract

Many applied researchers have to deal with spatially autocorrelated residuals (SAR). Available tests that identify spatial spillovers as captured by a significant SAR parameter, are either based on maximum likelihood (MLE) or generalized method of moments (GMM) estimates. This paper illustrates the properties of various tests for the null hypothesis of a zero SAR parameter in a comprehensive Monte Carlo study. The main finding is that Wald tests generally perform well regarding both size and power even in small samples. The GMM-based Wald test is correctly sized even for non-normally distributed disturbances and small samples, and it exhibits a similar power as its MLE-based counterpart. Hence, for the applied researcher the GMM Wald test can be recommended, because it is easy to implement.

Technical Details

RePEc Handle
repec:eee:regeco:v:39:y:2009:i:6:p:670-678
Journal Field
Urban
Author Count
4
Added to Database
2026-01-25