Forecasting with a panel Tobit model

• 成果类型: Article
• 署名作者: Liu, Laura; Moon, Hyungsik Roger; Schorfheide, Frank
• 署名单位: Indiana University System; Indiana University Bloomington; University of Southern California; Yonsei University; University of Pennsylvania; Center for Economic & Policy Research (CEPR); National Bureau of Economic Research
• 刊物名称: QUANTITATIVE ECONOMICS
• ISSN/ISSBN: 1759-7323
• DOI: 10.3982/QE1505
• 发表日期: 2023
• 页码: 117-159
• 关键词: Bayesian inference; density forecasts; loan charge-offs; panel data; set forecasts; Tobit model; C11; C14; C23; C53; G21

摘要

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.