# What is the difference between a Zero-inflated negative binomial regression and a Heckman-two-step regression model?

The title of my question says pretty much what I'm struggling to understand: What is the difference between a Zero-inflated negative binomial (ZINB) regression and a Heckman-two-step regression model? I get that the ZINB is for count data and the Heckit model usually goes with censored data. I still don't understand the difference. Both account for an overinflation of 0 and allow the calculation for the probability of getting a 0 on the dependent variable.

Are there advantages and disadvantages (especially when dealing with development aid data)?

I would greatly appreciate any explanation!

• can you provide a link to a description of the Heckmann-two-step regression model? I only find sources that refer to the Heckman Two-Step Correction for Selection Bias which is quite obviously something else than a negative-binomial regression. Commented Dec 10, 2019 at 13:02
• Its also known as Heckit model -> its a generalization of a Tobit model if I understood that correctly. Commented Dec 10, 2019 at 13:04
• Never heard of "Heckit". But to the question, the Heckman two-stage model, which uses a logistic or probit binary model in the first stage, allows for arbitrary clumping at zero. Zero-inflated single models can only inflate P(zero) so much. Commented Dec 10, 2019 at 13:30