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Forecasting with a Panel Tobit Model

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  • Laura Liu
  • Hyungsik Roger Moon
  • Frank Schorfheide

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.

Suggested Citation

  • Laura Liu & Hyungsik Roger Moon & Frank Schorfheide, 2021. "Forecasting with a Panel Tobit Model," Papers 2110.14117, arXiv.org, revised Jul 2022.
    • Handle: RePEc:arx:papers:2110.14117
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    References listed on IDEAS

    as
    1. Laura Liu & Hyungsik Roger Moon & Frank Schorfheide, 2023. "Forecasting with a panel Tobit model," Quantitative Economics, Econometric Society, vol. 14(1), pages 117-159, January.
    2. Laura Liu, 2018. "Density Forecasts in Panel Data Models : A Semiparametric Bayesian Perspective," Finance and Economics Discussion Series 2018-036, Board of Governors of the Federal Reserve System (U.S.).
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    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Boyuan Zhang, 2020. "Forecasting with Bayesian Grouped Random Effects in Panel Data," Papers 2007.02435, arXiv.org, revised Oct 2020.
    2. Timothy B. Armstrong & Michal Koles'ar & Mikkel Plagborg-M{o}ller, 2020. "Robust Empirical Bayes Confidence Intervals," Papers 2004.03448, arXiv.org, revised May 2022.
    3. Xin Sheng & Rangan Gupta & Qiang Ji, 2022. "Forecasting charge-off rates with a panel Tobit model: the role of uncertainty," Applied Economics Letters, Taylor & Francis Journals, vol. 29(10), pages 927-931, June.
    4. Laura Liu & Hyungsik Roger Moon & Frank Schorfheide, 2023. "Forecasting with a panel Tobit model," Quantitative Economics, Econometric Society, vol. 14(1), pages 117-159, January.
    5. Liu, Laura & Moon, Hyungsik Roger & Schorfheide, Frank, 2021. "Panel forecasts of country-level Covid-19 infections," Journal of Econometrics, Elsevier, vol. 220(1), pages 2-22.
    6. Zuoxiang Zhao & Hongjun Sun & Ding Han & Qiuyun Zhao, 2023. "Development strategy, technological progress, and regional environmental performance: empirical evidence from China," Economic Change and Restructuring, Springer, vol. 56(5), pages 3701-3732, October.
    7. Timothy B. Armstrong & Michal Kolesár & Mikkel Plagborg‐Møller, 2022. "Robust Empirical Bayes Confidence Intervals," Econometrica, Econometric Society, vol. 90(6), pages 2567-2602, November.
    8. Kim, Hyeongwoo & Son, Jisoo, 2023. "What Charge-Off Rates Are Predictable by Macroeconomic Latent Factors?," MPRA Paper 116880, University Library of Munich, Germany.
    9. Antonio Pacifico, 2023. "Obesity and labour market outcomes in Italy: a dynamic panel data evidence with correlated random effects," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 24(4), pages 557-574, June.
    10. Brezigar-Masten, Arjana & Masten, Igor & Volk, Matjaž, 2021. "Modelin-g credit risk with a Tobit model of days past due," Journal of Banking & Finance, Elsevier, vol. 122(C).
    11. Anna Bykhovskaya & James A. Duffy, 2022. "The Local to Unity Dynamic Tobit Model," Papers 2210.02599, arXiv.org, revised Feb 2023.
    12. James A. Duffy & Sophocles Mavroeidis & Sam Wycherley, 2022. "Cointegration with Occasionally Binding Constraints," Papers 2211.09604, arXiv.org, revised Jul 2023.
    13. Timmermann, Allan & Zhu, Yinchu, 2019. "Comparing Forecasting Performance with Panel Data," CEPR Discussion Papers 13746, C.E.P.R. Discussion Papers.

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    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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