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Building Spatial Choice Models from Aggregate Data

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  • Tommi Orpana
  • Jouko Lampinen

Abstract

Choice model construction is usually based on information about a number of separate choice situations, for which all relevant quantities are known. This paper concerns the case where only higher level, aggregate information is available about the choice results and the prevailing conditions. We demonstrate the applicability of a generic inverse parameter estimation method in estimating a model for grocery store choice. We also propose some enhancements to standard spatial choice models and demonstrate their applicability.

Suggested Citation

  • Tommi Orpana & Jouko Lampinen, 2003. "Building Spatial Choice Models from Aggregate Data," Journal of Regional Science, Wiley Blackwell, vol. 43(2), pages 319-348, May.
    • Handle: RePEc:bla:jregsc:v:43:y:2003:i:2:p:319-348
    DOI: 10.1111/1467-9787.00301
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    Cited by:

    1. Wieland, Thomas, 2014. "Räumliches Einkaufsverhalten und Standortpolitik im Einzelhandel unter Berücksichtigung von Agglomerationseffekten: Theoretische Erklärungsansätze, modellanalytische Zugänge und eine empirisch-ökonome," MPRA Paper 77163, University Library of Munich, Germany.
    2. de Grange, Louis & Troncoso, Rodrigo & Ibeas, Angel & González, Felipe, 2009. "Gravity model estimation with proxy variables and the impact of endogeneity on transportation planning," Transportation Research Part A: Policy and Practice, Elsevier, vol. 43(2), pages 105-116, February.
    3. Mahama-Musah, Fuseina & Schoutteet, Penelope & Vanhaverbeke, Lieselot, 2021. "Using online data for offline decisions: A geostatistical approach for evaluating the patronage potential of a baby-care retailer," Journal of Retailing and Consumer Services, Elsevier, vol. 62(C).
    4. Thomas Wieland, 2023. "Spatial shopping behavior during the Corona pandemic: insights from a micro-econometric store choice model for consumer electronics and furniture retailing in Germany," Journal of Geographical Systems, Springer, vol. 25(2), pages 291-326, April.
    5. Louis Grange & Felipe González & Juan Muñoz & Sebastián Raveau, 2014. "An Improved Stirling Approximation for Trip Distribution Models," Networks and Spatial Economics, Springer, vol. 14(3), pages 531-548, December.
    6. KURKALOVA, Lyubov A. & WADE, Tara R., 2013. "Aggregated Choice Data And Logit Models: Application To Environmental Benign Practices Of Conservation Tillage By Farmers In The State Of Iowa," Applied Econometrics and International Development, Euro-American Association of Economic Development, vol. 13(2), pages 119-128.
    7. M. De Beule & D. Van Den Poel & N. Van De Weghe, 2013. "An extended Huff-model for robustly benchmarking and predicting retail network performance," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/866, Ghent University, Faculty of Economics and Business Administration.

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