The Effect of Including Irrelevant Alternatives in Discrete Choice Models of Recreation Demand

John N. Ng’ombe () and B Brorsen
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John N. Ng’ombe: Oklahoma State University
Authors registered in the RePEc Author Service: John N. Ng'ombe

Computational Economics, 2022, vol. 60, issue 1, No 4, 97 pages

Abstract: Abstract We measure bias and efficiency of parameter estimates in the conditional logit (CL) and independent availability logit (IAL) models. Our Monte Carlo experiments consider both no choice set formation where individuals choose from the full set of alternatives, and when choice sets are stochastically formed and individuals choose from a subset of all alternatives. We also compare the performance of the two models using empirical data on paddlefish angler preferences and catch-and-release regulations in Oklahoma. Both the CL and IAL work well when their own assumptions hold, but not under the alternative’s assumptions. The IAL approximates the attribute-based cutoff well in empirical data. While neither the IAL nor the CL is universally preferred, based on our findings, we recommend the IAL when the true consideration sets are unknown.

Keywords: Choice set specification; Conditional logit; Independent availability logit; Monte Carlo experiments; Travel cost cutoffs (search for similar items in EconPapers)
JEL-codes: C25 C51 C52 Q5 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10614-021-10138-1

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