IDCeMPy: Python Package for Inflated Discrete Choice Models

Scholars and data scientists often use discrete choice models to evaluate ordered dependent variables using the ordered probit model and unordered polytomous outcome measures via the multinomial logit (MNL) estimator (Greene, 2002; Richards & Bonnet, 2018; Sarrias, 2016). These models, however, cannot account for the possibility that in many ordered and unordered polytomous choice outcomes, a disproportionate share of observations — stemming from two distinct data generating processes (DGPs) — fall into a single category which is thus “inflated.” For instance, ordered outcome measures of self-reported smoking behavior that range from 0 for “no smoking” to 3 for “smoking 20 cigarettes or more daily” contain excessive observations in the zero (no smoking) category that includes individuals who never smoke cigarettes and those who smoked previously but temporarily stop smoking because of an increase in cigarette costs (Greene et al., 2015; Harris & Zhao, 2007). The “indifference” middle-category in ordered measures of immigration attitudes is inflated since it includes respondents who are genuinely indifferent about immigration and those who select “indifference” because of social desirability reasons (Bagozzi & Mukherjee, 2012; Brown et al., 2020). The baseline category of unordered polytomous variables of presidential vote choice is also often inflated as it includes non-voters who abstain from voting owing to temporary factors and routine non-voters who are disengaged from the political process (Bagozzi & Marchetti, 2017; Campbell & Monson, 2008). Inflated discrete choice models have been developed to address such category inflation in ordered and unordered polytomous outcome variables as failing to do so leads to model misspecification and incorrect inferences (Bagozzi & Mukherjee, 2012; Brown et al., 2020; Harris & Zhao, 2007).