hurdlesbias

Hurdlesbias is a term used in statistics and econometrics to describe bias that can arise when applying hurdle models to zero-inflated or zero-valued data. A hurdle model conceptualizes the data-generating process in two stages: first, a binary decision about whether a positive outcome occurs (the hurdle), and second, conditional on crossing the hurdle, a distribution for the positive outcomes. The term highlights situations where the two-part structure introduces systematic errors in estimation or inference.

Common causes of hurdlesbias include misspecification of either component or incorrect assumptions about their relationship. If the hurdle model uses an incorrect link function for the binary part, or if the distribution assumed for positive values is misaligned with the data, estimates can be biased. Additional sources are dependence between the two stages that is ignored (for example, when the same unobserved factors influence both the hurdle and the positive outcome), omitted variables, measurement error, or non-random missing data that interact with the hurdle mechanism. Small samples or improper model comparison can also exacerbate bias.

Effects of hurdlesbias manifest as biased coefficient estimates, distorted standard errors, and miscalibrated predictive probabilities, which can lead to misguided conclusions or policy decisions. Detection relies on model diagnostics, goodness-of-fit checks, and simulation studies. Researchers may compare hurdle models with zero-inflated alternatives, test for dependence between stages, or use joint estimation and robust or Bayesian approaches to mitigate bias.

Related concepts include hurdle models, zero-inflated models, two-part models, and selection bias.