Forecasting with a Panel Tobit Model
We use a dynamic panel Tobit model with heteroskedasticity to generate point, set, and density 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. 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 charge-off rates for credit card and residential real estate loans, comparing various versions of the panel Tobit model.
We thank Mitchell Berlin, Siddhartha Chib, Tim Armstrong and participants at various seminars and conferences for helpful comments and suggestions. Moon and Schorfheide gratefully acknowledge financial support from the National Science Foundation under Grants SES 1625586 and SES 1424843, respectively. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.