PERSPECTIVES ON SPATIAL ECONOMETRICS: LINEAR SMOOTHING WITH STRUCTURED MODELS
Version of Record online: 3 JAN 2012
© 2012, Wiley Periodicals, Inc.
Journal of Regional Science
Volume 52, Issue 2, pages 192–209, May 2012
How to Cite
McMillen, D. P. (2012), PERSPECTIVES ON SPATIAL ECONOMETRICS: LINEAR SMOOTHING WITH STRUCTURED MODELS. Journal of Regional Science, 52: 192–209. doi: 10.1111/j.1467-9787.2011.00746.x
- Issue online: 19 APR 2012
- Version of Record online: 3 JAN 2012
- Received: August 2010; revised: August 2011; accepted: August 2011.
ABSTRACT Though standard spatial econometric models may be useful for specification testing, they rely heavily on a parametric structure that is highly sensitive to model misspecification. The commonly used spatial AR model is a form of spatial smoothing with a structure that closely resembles a semiparametric model. Nonparametric and semiparametric models are generally a preferable approach for more descriptive spatial analysis. Estimated population density functions illustrate the differences between the spatial AR model and nonparametric approaches to data smoothing. A series of Monte Carlo experiments demonstrates that nonparametric predicted values and marginal effect estimates are much more accurate then spatial AR models when the contiguity matrix is misspecified.