Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/296320 
Year of Publication: 
2023
Citation: 
[Journal:] Quantitative Economics [ISSN:] 1759-7331 [Volume:] 14 [Issue:] 1 [Year:] 2023 [Pages:] 117-159
Publisher: 
The Econometric Society, New Haven, CT
Abstract: 
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.
Subjects: 
Bayesian inference
density forecasts
loan charge-offs
panel data
set forecasts
Tobit model
JEL: 
C11
C14
C23
C53
G21
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc Logo
Document Type: 
Article

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