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Predictive Distributions based on Longitudinal Earnings Data

Author

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  • Gary Chamberlain
  • Keisuke Hirano

Abstract

Consider an individual trying to forecast his future earnings, in order to guide savings and other decisions. We envision an individual seeking advice from a financial planner. The individual provides data on his earnings history and on various personal characteristics such as age and education. The planner has access to longitudinal data sets that provide data on earnings histories and personal characteristics for samples of individuals. We devise optimal ways to combine the individual's information with the survey data in order to provide the individual with a conditional distribution for his future earnings. We work with data from the Panel Study of Income Dynamics, and our main modification of previous models is to allow for heterogeneity in volatility. This has important consequences, in that the spread of the predictive distribution becomes sensitive to the variability in the earnings history.

Suggested Citation

  • Gary Chamberlain & Keisuke Hirano, 1999. "Predictive Distributions based on Longitudinal Earnings Data," Annals of Economics and Statistics, GENES, issue 55-56, pages 211-242.
    • Handle: RePEc:adr:anecst:y:1999:i:55-56:p:211-242
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    Cited by:

    1. Fatih Guvenen & Fatih Karahan & Serdar Ozkan & Jae Song, 2015. "What Do Data on Millions of U.S. Workers Reveal about Life-Cycle Earnings Risk?," NBER Working Papers 20913, National Bureau of Economic Research, Inc.
    2. Costas Meghir & Luigi Pistaferri, 2004. "Income Variance Dynamics and Heterogeneity," Econometrica, Econometric Society, vol. 72(1), pages 1-32, January.
    3. L. Hospido, 2012. "Modelling heterogeneity and dynamics in the volatility of individual wages," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(3), pages 386-414, April.
    4. 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.).
    5. Raffaella Giacomini & Sokbae Lee & Silvia Sarpietro, 2023. "A Robust Method for Microforecasting and Estimation of Random Effects," Papers 2308.01596, arXiv.org.
    6. Botosaru, Irene, 2023. "Time-varying unobserved heterogeneity in earnings shocks," Journal of Econometrics, Elsevier, vol. 235(2), pages 1378-1393.
    7. Jeffrey Brown & Chichun Fang & Francisco Gomes, 2012. "Risk and Returns to Education," NBER Working Papers 18300, National Bureau of Economic Research, Inc.
    8. Stéphane Bonhomme & Jean-Marc Robin, 2010. "Generalized Non-Parametric Deconvolution with an Application to Earnings Dynamics," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 77(2), pages 491-533.
    9. Joseph Altonji & Disa Hynsjo & Ivan Vidangos, 2023. "Individual Earnings and Family Income: Dynamics and Distribution," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 49, pages 225-250, July.
    10. Stephen Jenkins & Peter Lambert, 2011. "Robert Moffitt and Peter Gottschalk’s 1995 paper ‘Trends in the covariance structure of earnings in the US: 1969–1987’," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 9(3), pages 433-437, September.
    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. Baltagi, Badi H., 2013. "Panel Data Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 995-1024, Elsevier.
    13. Alfonso A. Irarrazabal & Lin Ma & Juan Carlos Parra-Alvarez, 2023. "Optimal asset allocation for commodity sovereign wealth funds," Quantitative Finance, Taylor & Francis Journals, vol. 23(3), pages 471-495, March.
    14. Siqi Wei, 2022. "Income, Employment and Health Risks of Older Workers," Working Papers wp2022_2205, CEMFI.
    15. Badi H. Baltagi, 2008. "Forecasting with panel data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(2), pages 153-173.
    16. Fatih Guvenen & Fatih Karahan & Serdar Ozkan & Jae Song, 2021. "What Do Data on Millions of U.S. Workers Reveal About Lifecycle Earnings Dynamics?," Econometrica, Econometric Society, vol. 89(5), pages 2303-2339, September.
    17. Gary Chamberlain, 2000. "Econometric applications of maxmin expected utility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(6), pages 625-644.
    18. Rusty Tchernis, 2010. "Measuring Human Capital And Its Effects On Wage Growth," Journal of Economic Surveys, Wiley Blackwell, vol. 24(2), pages 362-387, April.
    19. Hospido, Laura, 2015. "Wage dynamics in the presence of unobserved individual and job heterogeneity," Labour Economics, Elsevier, vol. 33(C), pages 81-93.
    20. Norets, Andriy & Pelenis, Justinas, 2022. "Adaptive Bayesian estimation of conditional discrete-continuous distributions with an application to stock market trading activity," Journal of Econometrics, Elsevier, vol. 230(1), pages 62-82.
    21. Laura Hospido, 2009. "Job changes and individual-job specific wage dynamics," Working Papers 0907, Banco de España.
    22. 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.
    23. Fatih Guvenen & Fatih Karahan & Serdar Ozkan, 2018. "Consumption and Savings Under Non-Gaussian Income Risk," 2018 Meeting Papers 314, Society for Economic Dynamics.

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