Forecasting with a Panel Tobit Model

Laura Liu, Hyungsik Roger Moon and Frank Schorfheide

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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.

Date: 2021-10, Revised 2022-07
New Economics Papers: this item is included in nep-ban and nep-for
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http://arxiv.org/pdf/2110.14117 Latest version (application/pdf)

Related works:
Journal Article: Forecasting with a panel Tobit model (2023)
Working Paper: Forecasting with a Panel Tobit Model (2019)
Working Paper: Forecasting with a Panel Tobit Model (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2110.14117

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