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Forecasting with a Panel Tobit Model

Laura Liu (), Hyungsik Moon () and Frank Schorfheide ()

No 2019-005, CAEPR Working Papers from Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington

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 coeffients 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 (search for similar items in EconPapers)
JEL-codes: C11 C14 C23 C53 G21 (search for similar items in EconPapers)
Pages: 58 pages
Date: 2019-05
New Economics Papers: this item is included in nep-ban, nep-ecm, nep-for and nep-ore
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https://caepr.indiana.edu/RePEc/inu/caeprp/CAEPR2019-005.pdf (application/pdf)

Related works:
Working Paper: Forecasting with a Panel Tobit Model (2022) Downloads
Working Paper: Forecasting with a Panel Tobit Model (2019) Downloads
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