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.
|Number of pages||43|
|Publication status||Published - 2023 Jan|
Bibliographical notePublisher Copyright:
Copyright © 2023 The Authors.
All Science Journal Classification (ASJC) codes
- Economics and Econometrics