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Traditional ordered outcome models, such as ordered logit and probit, are widely employed to analyze the effects of independent variables on ordered response data. These models assume fixed thresholds across observations, limiting their flexibility. The generalized ordered response (GOR) model overcomes this constraint by allowing thresholds to vary as a function of independent variables. Additionally, it can be extended to account for unobserved heterogeneity, similar to mixed ordered logit/probit models. This chapter presents a detailed formulation of the mixed generalized ordered response (MGOR) model and its application to US airport-level airline demand. Analyzing quarterly passenger demand data from 300 airports (2010–2018) in GAUSS, we demonstrate how the mixed generalized ordered logit (MGOL) model captures variations in air travel demand.

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Generalized Ordered Logit Model: Application to Airline Demand Modeling in the United States

  • 章节

  • 发表日期：

    2026年

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  • Sudipta Dey Tirtha,
  • Naveen Eluru

摘要

Traditional ordered outcome models, such as ordered logit and probit, are widely employed to analyze the effects of independent variables on ordered response data. These models assume fixed thresholds across observations, limiting their flexibility. The generalized ordered response (GOR) model overcomes this constraint by allowing thresholds to vary as a function of independent variables. Additionally, it can be extended to account for unobserved heterogeneity, similar to mixed ordered logit/probit models. This chapter presents a detailed formulation of the mixed generalized ordered response (MGOR) model and its application to US airport-level airline demand. Analyzing quarterly passenger demand data from 300 airports (2010–2018) in GAUSS, we demonstrate how the mixed generalized ordered logit (MGOL) model captures variations in air travel demand.