Forecasting with a Panel Tobit Model
58 Pages Posted: 3 Jan 2020
Date Written: December 10, 2019
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.
Keywords: Bayesian Inference, Density Forecasts, Interval Forecasts, Loan Charge-Oﬀs, Panel Data, Point Forecasts, Set Forecasts, Tobit Model
JEL Classification: C11, C14, C23, C53, G21
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