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Spatial Limited Dependent Variable Models: A Review Focused On Specification, Estimation, And Health Economics Applications

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  • Anna Gloria Billé
  • Giuseppe Arbia

Abstract

Modeling individual choices is one of the main aim in microeconometrics. Discrete choice models have been widely used to describe economic agents' utility functions and most of them play a paramount role in applied health economics. On the other hand, spatial econometrics collects a series of econometric tools, which are particularly useful when we deal with spatially distributed data sets. Accounting for spatial dependence can avoid inconsistency problems of the commonly used statistical estimators. However, the complex structure of spatial dependence in most of the nonlinear models still precludes a large diffusion of these spatial techniques. The purpose of this paper is then twofold. The former is to review the main methodological problems and their different solutions in spatial nonlinear modeling. The latter is to review their applications to health issues, especially those appeared in the last few years, by highlighting the main reasons why spatial discrete neighboring effects should be considered and suggesting possible future lines of development in this emerging field. Particular attention has been paid to cross‐sectional spatial discrete choice modeling. However, discussions on the main methodological advancements in other spatial limited dependent variable models and spatial panel data models are also included.

Suggested Citation

  • Anna Gloria Billé & Giuseppe Arbia, 2019. "Spatial Limited Dependent Variable Models: A Review Focused On Specification, Estimation, And Health Economics Applications," Journal of Economic Surveys, Wiley Blackwell, vol. 33(5), pages 1531-1554, December.
    • Handle: RePEc:bla:jecsur:v:33:y:2019:i:5:p:1531-1554
    DOI: 10.1111/joes.12333
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    Cited by:

    1. Felipa de Mello-Sampayo, 2020. "Spatial Interaction Model for Healthcare Accessibility: What Scale Has to Do with It," Sustainability, MDPI, vol. 12(10), pages 1-19, May.
    2. Haoying Wang & Guohui Wu, 2022. "Modeling discrete choices with large fine-scale spatial data: opportunities and challenges," Journal of Geographical Systems, Springer, vol. 24(3), pages 325-351, July.
    3. Mondal, Aupal & Bhat, Chandra R., 2022. "A spatial rank-ordered probit model with an application to travel mode choice," Transportation Research Part B: Methodological, Elsevier, vol. 155(C), pages 374-393.
    4. Stefano Mainardi, 2021. "Preference heterogeneity, neighbourhood effects and basic services: logit kernel models for farmers’ climate adaptation in Ethiopia," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(5), pages 6869-6912, May.
    5. Nobel, Anne & Lizin, Sebastien & Malina, Robert, 2023. "What drives the designation of protected areas? Accounting for spatial dependence using a composite marginal likelihood approach," Ecological Economics, Elsevier, vol. 205(C).
    6. Kei Kabaya, 2022. "Examining spatially heterogeneous preferences for coastal ecosystem restoration with Bayesian spatial probit approaches," Letters in Spatial and Resource Sciences, Springer, vol. 15(1), pages 99-112, April.

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