6. Behavioral Insights from Choice Models

This chapter delves into the practical applications of discrete choice models, particularly the multinomial logit model, to understand and forecast behavior. Building on the skills acquired in estimating these models, the chapter explores how to infer preferences and predict behavioral changes under different conditions. It provides a step-by-step process for model estimation and scenario analysis, highlighting the importance of systematic approaches and the limitations of model predictions. The chapter also offers insights into the interpretation of model coefficients, marginal effects, elasticity, and willingness to pay, making it a valuable resource for professionals seeking to apply these models in real-world contexts.