Working Paper: NBER ID: w26569
Authors: Laura Liu; Hyungsik Roger Moon; Frank Schorfheide
Abstract: 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: Dynamic Panel Tobit Model; Heteroskedasticity; Bayesian Forecasting; Chargeoff Rates
JEL Codes: C11; C14; C23; C53; G21
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Inclusion of heterogeneity (C21) | Improved forecast accuracy (C53) |
| Modeling heteroskedasticity (C51) | Enhanced density and set forecast performance (C53) |
| Bayesian approach (C11) | Reliable forecast intervals (C53) |
| Predictors (local house prices and unemployment rates) (R21) | Chargeoff rates (G32) |