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Title: Forecasting with a panel tobit model Authors:  Laura Liu - Indiana University Bloomington (United States) [presenting]
Roger Moon - University of Southern California (United States)
Frank Schorfheide - University of Pennsylvania (United States)
Abstract: A dynamic panel Tobit model with heteroskedasticity is used 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 constructing 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.