I'm developing a model in R estimating vehicle miles traveled (VMT) using Tobit specification, since my data include a cluster of zero VMT values.
I understand that the coefficients given by a Tobit regression relate to an uncensored latent variable; however, since VMT actually cannot dip below zero, I'm interested in effects on the censored variable. I've found that typically the marginal effects are used to discuss Tobit results in this case vs. the regression coefficients.
For predicting values, I know one can make adjustments to generate expected values of the censored latent variable (as outlined here). In R, the VGAM package also allows you to specify type.fitted ="censored" for Tobit; when then fed into the predict function, it provides estimates for the censored latent variable as well.
My question is, is using these censored-variable estimates practical in generating real-world predictions, or is shifting the predicted values to reflect a censored latent variable more of an academic exercise? Hopefully that makes some sense -- I just haven't seen Tobit models used predictively anywhere, and I'm trying to generate how shifts in my independent variables will actually impact VMT from predicted values.
Thank you! This is my first Stack Exchange question, so forgive any mistakes here (and obviously I'd happily incorporate any changes you might suggest to future questions).