  We use a dynamic panel Tobit model with heteroskedasticity to generate
forecasts for a large cross-section of short time series of censored
observations. Our fully Bayesian approach allows us to flexibly estimate the
cross-sectional distribution of heterogeneous coefficients and then implicitly
use this distribution as prior to construct Bayes forecasts for the individual
time series. In addition to density forecasts, we construct set forecasts that
explicitly target the average coverage probability for the cross-section. We
present a novel application in which we forecast bank-level loan charge-off
rates for small banks.
