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Investigating the predictive capabilities of discrete choice models in the presence of spatial effects

Author

Listed:
  • Robertson, Richard D.
  • Nelson, Gerald C.
  • De Pinto, Alessandro

Abstract

Fully spatial treatments of discrete choice models are difficult to specify and can be computationally intense. Several ad hoc procedures have been proposed in the land use literature to mitigate the potential estimation problems caused by spatial effects. We investigate the consequences of different spatial effects for several of these non†fully†spatial approaches using Monte Carlo simulation of a binary choice and a real world example of land use on the island of Sumatra in Indonesia. We find that there are few effects on categorical prediction and that inclusion of spatially lagged explanatory variables is the most effective procedure.

Suggested Citation

Handle: RePEc:eee:paresc:v:88:y:2009:i:2:p:367-389
DOI: 10.1111/j.1435-5957.2009.00247.x
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JEL classification:

  • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
  • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
  • Q15 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Land Ownership and Tenure; Land Reform; Land Use; Irrigation; Agriculture and Environment
  • R14 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Land Use Patterns

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