Choice sets for spatial discrete choice models in data rich environments

Robert L. Hicks, Daniel S. Holland, Peter T. Kuriyama and Kurt E. Schnier

Resource and Energy Economics, 2020, vol. 60, issue C

Abstract: Failure to properly specify an agent's choice set in discrete choice models will generate biased parameter estimates resulting in inaccurate behavioral predictions as well as biased estimates of policy relevant metrics. We propose a method of constructing choice sets by sampling from specific points in space to model agent behavior when choice alternatives are unknown to the researcher, potentially infinite, and differ according to spatial and temporal factors. Using Monte Carlo analysis we compare the performance of this point-based sampling method to the commonly used approach of spatially aggregating choice alternatives. We then apply these alternative approaches to modelling location choice in the Pacific groundfish trawl fishery which has a complex spatial choice structure. Both the Monte Carlo and application results provide considerable support for the efficacy of the point-based approaches.

Keywords: Discrete choice; Consideration set; Random utility model; Spatial discrete choice (search for similar items in EconPapers)
JEL-codes: C25 C53 Q22 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:resene:v:60:y:2020:i:c:s0928765518304408

DOI: 10.1016/j.reseneeco.2019.101148

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