IDEAS home Printed from https://ideas.repec.org/a/wly/quante/v14y2023i1p117-159.html
   My bibliography  Save this article

Forecasting with a panel Tobit model

Author

Listed:
  • 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, 2023. "Forecasting with a panel Tobit model," Quantitative Economics, Econometric Society, vol. 14(1), pages 117-159, January.
    • Handle: RePEc:wly:quante:v:14:y:2023:i:1:p:117-159
    DOI: 10.3982/QE1505
    as

    Download full text from publisher

    File URL: https://doi.org/10.3982/QE1505
    Download Restriction: no
    File URL: https://libkey.io/10.3982/QE1505?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Arellano, Manuel & Bover, Olympia, 1995. "Another look at the instrumental variable estimation of error-components models," Journal of Econometrics, Elsevier, vol. 68(1), pages 29-51, July.
    2. Laura Liu & Hyungsik Roger Moon & Frank Schorfheide, 2020. "Forecasting With Dynamic Panel Data Models," Econometrica, Econometric Society, vol. 88(1), pages 171-201, January.
    3. 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.
    4. 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.
    5. Ross Askanazi & Francis X. Diebold & Frank Schorfheide & Minchul Shin, 2018. "On the Comparison of Interval Forecasts," Journal of Time Series Analysis, Wiley Blackwell, vol. 39(6), pages 953-965, November.
    6. Martin Burda & Matthew Harding, 2013. "Panel Probit With Flexible Correlated Effects: Quantifying Technology Spillovers In The Presence Of Latent Heterogeneity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(6), pages 956-981, September.
    7. Michael Keane & Olena Stavrunova, 2011. "A smooth mixture of Tobits model for healthcare expenditure," Health Economics, John Wiley & Sons, Ltd., vol. 20(9), pages 1126-1153, September.
    8. Keisuke Hirano, 2002. "Semiparametric Bayesian Inference in Autoregressive Panel Data Models," Econometrica, Econometric Society, vol. 70(2), pages 781-799, March.
    9. Del Negro, Marco & Schorfheide, Frank & Smets, Frank & Wouters, Rafael, 2007. "On the Fit of New Keynesian Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 123-143, April.
    10. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2015. "Prior Selection for Vector Autoregressions," The Review of Economics and Statistics, MIT Press, vol. 97(2), pages 436-451, May.
    11. Schorfheide, Frank, 2005. "VAR forecasting under misspecification," Journal of Econometrics, Elsevier, vol. 128(1), pages 99-136, September.
    12. Tong Li & Xiaoyong Zheng, 2008. "Semiparametric Bayesian inference for dynamic Tobit panel data models with unobserved heterogeneity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(6), pages 699-728.
    13. Chib, Siddhartha & Hamilton, Barton H., 2002. "Semiparametric Bayes analysis of longitudinal data treatment models," Journal of Econometrics, Elsevier, vol. 110(1), pages 67-89, September.
    14. 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.).
    15. Steven Wei, 1999. "A bayesian approach to dynamic tobit models," Econometric Reviews, Taylor & Francis Journals, vol. 18(4), pages 417-439.
    16. Peter E. Rossi, 2014. "Bayesian Non- and Semi-parametric Methods and Applications," Economics Books, Princeton University Press, edition 1, number 10259.
    17. Chib, Siddhartha & Jeliazkov, Ivan, 2006. "Inference in Semiparametric Dynamic Models for Binary Longitudinal Data," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 685-700, June.
    18. Z. I. Botev, 2017. "The normal law under linear restrictions: simulation and estimation via minimax tilting," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(1), pages 125-148, January.
    19. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
    20. Ishwaran H. & James L. F, 2001. "Gibbs Sampling Methods for Stick Breaking Priors," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 161-173, March.
    21. Fisher, Mark & Jensen, Mark J., 2022. "Bayesian nonparametric learning of how skill is distributed across the mutual fund industry," Journal of Econometrics, Elsevier, vol. 230(1), pages 131-153.
    22. Ghosal,Subhashis & van der Vaart,Aad, 2017. "Fundamentals of Nonparametric Bayesian Inference," Cambridge Books, Cambridge University Press, number 9780521878265, September.
    23. Baranchuk, Nina & Chib, Siddhartha, 2008. "Assessing the role of option grants to CEOs: How important is heterogeneity?," Journal of Empirical Finance, Elsevier, vol. 15(2), pages 145-166, March.
    24. Ghosh, Amit, 2015. "Banking-industry specific and regional economic determinants of non-performing loans: Evidence from US states," Journal of Financial Stability, Elsevier, vol. 20(C), pages 93-104.
    25. Moyer, Susan E., 1990. "Capital adequacy ratio regulations and accounting choices in commercial banks," Journal of Accounting and Economics, Elsevier, vol. 13(2), pages 123-154, July.
    26. Chib, Siddhartha, 1992. "Bayes inference in the Tobit censored regression model," Journal of Econometrics, Elsevier, vol. 51(1-2), pages 79-99.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Boyuan Zhang, 2020. "Forecasting with Bayesian Grouped Random Effects in Panel Data," Papers 2007.02435, arXiv.org, revised Oct 2020.
    2. 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.
    3. Chen, Mo & Xue, Wei-Xian & Zhao, Xin-Xin & Chang, Chun-Ping & Liu, Xiaoxia, 2024. "The impact of economic sanctions on the COVID-19 pandemic," Economic Analysis and Policy, Elsevier, vol. 82(C), pages 163-174.
    4. Timothy B. Armstrong & Michal Kolesár & Mikkel Plagborg‐Møller, 2022. "Robust Empirical Bayes Confidence Intervals," Econometrica, Econometric Society, vol. 90(6), pages 2567-2602, November.
    5. Kim, Hyeongwoo & Son, Jisoo, 2024. "What charge-off rates are predictable by macroeconomic latent factors?," Journal of Financial Stability, Elsevier, vol. 74(C).
    6. 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.
    7. Qu, Ritong & Timmermann, Allan & Zhu, Yinchu, 2023. "Comparing forecasting performance in cross-sections," Journal of Econometrics, Elsevier, vol. 237(2).
    8. Anna Bykhovskaya & James A. Duffy, 2022. "The Local to Unity Dynamic Tobit Model," Papers 2210.02599, arXiv.org, revised May 2024.
    9. 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.
    10. 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.
    11. 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.
    12. Bykhovskaya, Anna & Duffy, James A., 2024. "The local to unity dynamic Tobit model," Journal of Econometrics, Elsevier, vol. 241(2).
    13. 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.
    14. 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).
    15. James A. Duffy & Sophocles Mavroeidis & Sam Wycherley, 2022. "Cointegration with Occasionally Binding Constraints," Papers 2211.09604, arXiv.org, revised Jul 2023.
    16. Timmermann, Allan & Zhu, Yinchu, 2019. "Comparing Forecasting Performance with Panel Data," CEPR Discussion Papers 13746, C.E.P.R. Discussion Papers.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.).
    2. Genya Kobayashi & Hideo Kozumi, 2012. "Bayesian analysis of quantile regression for censored dynamic panel data," Computational Statistics, Springer, vol. 27(2), pages 359-380, June.
    3. Tong Li & Xiaoyong Zheng, 2008. "Semiparametric Bayesian inference for dynamic Tobit panel data models with unobserved heterogeneity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(6), pages 699-728.
    4. 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.
    5. Boyuan Zhang, 2020. "Forecasting with Bayesian Grouped Random Effects in Panel Data," Papers 2007.02435, arXiv.org, revised Oct 2020.
    6. Federico Bassetti & Roberto Casarin & Marco Del Negro, 2022. "A Bayesian Approach to Inference on Probabilistic Surveys," Staff Reports 1025, Federal Reserve Bank of New York.
    7. Abel Rodriguez & Enrique ter Horst, 2008. "Measuring expectations in options markets: An application to the SP500 index," Papers 0901.0033, arXiv.org.
    8. Gael M. Martin & David T. Frazier & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2023. "Bayesian Forecasting in the 21st Century: A Modern Review," Monash Econometrics and Business Statistics Working Papers 1/23, Monash University, Department of Econometrics and Business Statistics.
    9. Laura Liu, 2017. "Density Forecasts in Panel Models: A semiparametric Bayesian Perspective," PIER Working Paper Archive 17-006, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 28 Apr 2017.
    10. Griffin, J. E. & Steel, M. F. J., 2004. "Semiparametric Bayesian inference for stochastic frontier models," Journal of Econometrics, Elsevier, vol. 123(1), pages 121-152, November.
    11. Laura Liu & Hyungsik Roger Moon & Frank Schorfheide, 2020. "Forecasting With Dynamic Panel Data Models," Econometrica, Econometric Society, vol. 88(1), pages 171-201, January.
    12. Bassetti, Federico & Casarin, Roberto & Leisen, Fabrizio, 2014. "Beta-product dependent Pitman–Yor processes for Bayesian inference," Journal of Econometrics, Elsevier, vol. 180(1), pages 49-72.
    13. Nalan Basturk & Cem Cakmakli & S. Pinar Ceyhan & Herman K. van Dijk, 2014. "On the Rise of Bayesian Econometrics after Cowles Foundation Monographs 10, 14," Tinbergen Institute Discussion Papers 14-085/III, Tinbergen Institute, revised 04 Sep 2014.
    14. Jaeho Kim & Le Wang, 2019. "Hidden group patterns in democracy developments: Bayesian inference for grouped heterogeneity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(6), pages 1016-1028, September.
    15. Martin, Gael M. & Frazier, David T. & Maneesoonthorn, Worapree & Loaiza-Maya, Rubén & Huber, Florian & Koop, Gary & Maheu, John & Nibbering, Didier & Panagiotelis, Anastasios, 2024. "Bayesian forecasting in economics and finance: A modern review," International Journal of Forecasting, Elsevier, vol. 40(2), pages 811-839.
    16. Anders Warne & Günter Coenen & Kai Christoffel, 2017. "Marginalized Predictive Likelihood Comparisons of Linear Gaussian State‐Space Models with Applications to DSGE, DSGE‐VAR, and VAR Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(1), pages 103-119, January.
    17. Boyuan Zhang, 2022. "Incorporating Prior Knowledge of Latent Group Structure in Panel Data Models," Papers 2211.16714, arXiv.org, revised Oct 2023.
    18. 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.
    19. Todd E. Clark & Michael W. McCracken, 2013. "Evaluating the accuracy of forecasts from vector autoregressions," Working Papers 2013-010, Federal Reserve Bank of St. Louis.
    20. Jensen, Mark J. & Maheu, John M., 2010. "Bayesian semiparametric stochastic volatility modeling," Journal of Econometrics, Elsevier, vol. 157(2), pages 306-316, August.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:quante:v:14:y:2023:i:1:p:117-159. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/essssea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.