Two-Stage Hurdle Models: Predicting Zero-Inflated Outcomes

Zero-inflated outcomes, where a large portion of data is zero, present a significant challenge for standard regression models. These traditional methods often fail because they cannot properly handle the distinct processes generating zero versus positive values. A two-stage hurdle model provides a more effective solution by decomposing the problem into two distinct steps: a classification model to predict if an outcome is positive, and a regression model to predict the magnitude given that it is positive. The final unconditional prediction is then calculated by multiplying the probability of a positive outcome from the first stage by the expected value from the second stage.