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

Author

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  • Richard D. Robertson
  • Gerald C. Nelson
  • Alessandro De Pinto
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. Resumen Los enfoques totalmente espaciales de modelos de elección discreta son difíciles de especificar y su cálculo puede ser muy laborioso. En la literatura sobre uso del suelo se han propuesto varios procedimientos para el caso con el fin de mitigar los posibles problemas de estimación causados por efectos espaciales. Investigamos las consecuencias de diferentes efectos espaciales para varios de estos enfoques no totalmente espaciales, utilizando simulación de Monte Carlo de elección binaria y un ejemplo del mundo real de uso del suelo en la isla de Sumatra en Indonesia. Encontramos que existen pocos efectos sobre la predicción categórica y que la incorporación de variables explicativas espacialmente desfasadas es el procedimiento más efectivo.

Suggested Citation

  • Richard D. Robertson & Gerald C. Nelson & Alessandro De Pinto, 2009. "Investigating the predictive capabilities of discrete choice models in the presence of spatial effects," Papers in Regional Science, Wiley Blackwell, vol. 88(2), pages 367-388, June.
    • Handle: RePEc:bla:presci:v:88:y:2009:i:2:p:367-388
    DOI: 10.1111/j.1435-5957.2009.00247.x
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    1. Jacob M. Markman & Eric A. Hanushek & John F. Kain & Steven G. Rivkin, 2003. "Does peer ability affect student achievement?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(5), pages 527-544.
    2. Case, Anne C, 1991. "Spatial Patterns in Household Demand," Econometrica, Econometric Society, vol. 59(4), pages 953-965, July.
    3. Carmen Carrión-Flores & Elena G. Irwin, 2004. "Determinants of Residential Land-Use Conversion and Sprawl at the Rural-Urban Fringe," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(4), pages 889-904.
    4. Chomitz, Kenneth M & Gray, David A, 1996. "Roads, Land Use, and Deforestation: A Spatial Model Applied to Belize," The World Bank Economic Review, World Bank, vol. 10(3), pages 487-512, September.
    5. Garry F. Barrett & Stephen G. Donald, 2003. "Consistent Tests for Stochastic Dominance," Econometrica, Econometric Society, vol. 71(1), pages 71-104, January.
    6. Gerald C. Nelson & Daniel Hellerstein, 1997. "Do Roads Cause Deforestation? Using Satellite Images in Econometric Analysis of Land Use," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 79(1), pages 80-88.
    7. Kathleen P. Bell & Nancy E. Bockstael, 2000. "Applying the Generalized-Moments Estimation Approach to Spatial Problems Involving Microlevel Data," The Review of Economics and Statistics, MIT Press, vol. 82(1), pages 72-82, February.
    8. Staal, S. J. & Baltenweck, I. & Waithaka, M. M. & deWolff, T. & Njoroge, L., 2002. "Location and uptake: integrated household and GIS analysis of technology adoption and land use, with application to smallholder dairy farms in Kenya," Agricultural Economics, Blackwell, vol. 27(3), pages 295-315, November.
    9. Baltagi, Badi H. & Egger, Peter & Pfaffermayr, Michael, 2007. "Estimating models of complex FDI: Are there third-country effects?," Journal of Econometrics, Elsevier, vol. 140(1), pages 260-281, September.
    10. Akaike, Hirotugu, 1981. "Likelihood of a model and information criteria," Journal of Econometrics, Elsevier, vol. 16(1), pages 3-14, May.
    11. Shroder, Mark, 1995. "Games the States Don't Play: Welfare Benefits and the Theory of Fiscal Federalism," The Review of Economics and Statistics, MIT Press, vol. 77(1), pages 183-191, February.
    12. Gerald C. Nelson & GVirginia Harris & Steven W. Stone, 2001. "Deforestation, Land Use, and Property Rights: Empirical Evidence from Darién, Panama," Land Economics, University of Wisconsin Press, vol. 77(2), pages 187-205.
    13. Luc Anselin, 2003. "Spatial Externalities, Spatial Multipliers, And Spatial Econometrics," International Regional Science Review, , vol. 26(2), pages 153-166, April.
    14. Bhat, Chandra R. & Guo, Jessica, 2004. "A mixed spatially correlated logit model: formulation and application to residential choice modeling," Transportation Research Part B: Methodological, Elsevier, vol. 38(2), pages 147-168, February.
    15. Adina M. Merenlender, 2006. "Habitat and Open Space at Risk of Land-Use Conversion: Targeting Strategies for Land Conservation," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 88(1), pages 28-42.
    16. Maureen Cropper & Jyotsna Puri & Charles Griffiths, 2001. "Predicting the Location of Deforestation: The Role of Roads and Protected Areas in North Thailand," Land Economics, University of Wisconsin Press, vol. 77(2), pages 172-186.
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    2. T. Randall Fortenbery & Steven C. Deller & Lindsay Amiel, 2013. "The Location Decisions of Biodiesel Refineries," Land Economics, University of Wisconsin Press, vol. 89(1), pages 118-136.
    3. Michael Brady & Elena Irwin, 2011. "Accounting for Spatial Effects in Economic Models of Land Use: Recent Developments and Challenges Ahead," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 48(3), pages 487-509, March.
    4. Avery S. Cohn & Juliana Gil & Thomas Berger & Heitor Pellegrina & Chantal Toledo, "undated". "Patterns and Processes of Pasture to Crop Conversion in Brazil: Evidence from Mato Grosso State," Mathematica Policy Research Reports 8ce99775615f42b98ff43f530, Mathematica Policy Research.
    5. Levente Tímár, 2011. "Rural Land Use and Land Tenure in New Zealand," Working Papers 11_13, Motu Economic and Public Policy Research.
    6. Villoria, Nelson B. & Liu, Jing, 2018. "Using spatially explicit data to improve our understanding of land supply responses: An application to the cropland effects of global sustainable irrigation in the Americas," Land Use Policy, Elsevier, vol. 75(C), pages 411-419.
    7. JunJie Wu & Monica Fisher & Unai Pascual, 2011. "Urbanization and the Viability of Local Agricultural Economies," Land Economics, University of Wisconsin Press, vol. 87(1), pages 109-125.

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