Spatial Correlation Robust Inference with Errors in Location or Distance

Timothy Conley and Francesca Molinari

Working Papers from Cornell University, Center for Analytic Economics

Abstract: This paper presents results from a Monte Carlo study concerning inference with spatially dependent data. We investigate the impact of location/distance measurement errors upon the accuracy of parametric and nonparametric estimators of asymptotic variances. Nonparametric estimators are quite robust to such errors, method of moments estimators perform surprisingly well, and MLE estimators are very poor. We also present and evaluate a specification test based on a parametric bootstrap that has good power properties for the types of measurement error we consider.

Date: 2005-02
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Citations: View citations in EconPapers (5)

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https://cae.economics.cornell.edu/05-12.pdf

Related works:
Journal Article: Spatial correlation robust inference with errors in location or distance (2007)
Working Paper: Spatial correlation robust inference with Errors in Location or Distance (2005)
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Persistent link: https://EconPapers.repec.org/RePEc:ecl:corcae:05-12

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