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Time-varying unobserved heterogeneity in earnings shocks

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  • Botosaru, Irene

Abstract

This paper considers the transitory-permanent model for the earnings process, and allows for time-varying individual-specific unobserved heterogeneity in each shock. The cross-sectional heterogeneity in each shock is drawn from an unknown distribution at each time period. Sufficient conditions for the nonparametric identification of the cross-sectional density functions of the heterogeneity are provided, under different assumptions on the time series behavior of the transitory shock. The method proposed is then applied to earnings data to document a high degree of cross-sectional heterogeneity in each shock.

Suggested Citation

  • Botosaru, Irene, 2023. "Time-varying unobserved heterogeneity in earnings shocks," Journal of Econometrics, Elsevier, vol. 235(2), pages 1378-1393.
    • Handle: RePEc:eee:econom:v:235:y:2023:i:2:p:1378-1393
    DOI: 10.1016/j.jeconom.2022.08.012
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    1. Yongsung Chang & Jay Hong & Marios Karabarbounis & Yicheng Wang & Tao Zhang, 2022. "Income Volatility and Portfolio Choices," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 44, pages 65-90, April.
    2. Primiceri, Giorgio E. & van Rens, Thijs, 2009. "Heterogeneous life-cycle profiles, income risk and consumption inequality," Journal of Monetary Economics, Elsevier, vol. 56(1), pages 20-39, January.
    3. Botosaru, Irene & Sasaki, Yuya, 2018. "Nonparametric heteroskedasticity in persistent panel processes: An application to earnings dynamics," Journal of Econometrics, Elsevier, vol. 203(2), pages 283-296.
    4. 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.
    5. Martin Browning & Mette Ejrnæs & Javier Alvarez, 2010. "Modelling Income Processes with Lots of Heterogeneity," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 77(4), pages 1353-1381.
    6. Zinde-Walsh, Victoria, 2014. "Measurement Error And Deconvolution In Spaces Of Generalized Functions," Econometric Theory, Cambridge University Press, vol. 30(6), pages 1207-1246, December.
    7. Ben-Moshe, Dan, 2018. "Identification Of Joint Distributions In Dependent Factor Models," Econometric Theory, Cambridge University Press, vol. 34(1), pages 134-165, February.
    8. 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.
    9. Blundell, Richard & Graber, Michael & Mogstad, Magne, 2015. "Labor income dynamics and the insurance from taxes, transfers, and the family," Journal of Public Economics, Elsevier, vol. 127(C), pages 58-73.
    10. Benjamin Williams, 2020. "Identification of the linear factor model," Econometric Reviews, Taylor & Francis Journals, vol. 39(1), pages 92-109, January.
    11. Shin, Donggyun & Solon, Gary, 2011. "Trends in men's earnings volatility: What does the Panel Study of Income Dynamics show?," Journal of Public Economics, Elsevier, vol. 95(7-8), pages 973-982, August.
    12. Keisuke Hirano, 2002. "Semiparametric Bayesian Inference in Autoregressive Panel Data Models," Econometrica, Econometric Society, vol. 70(2), pages 781-799, March.
    13. 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.
    14. Centorrino Samuele & Feve Frederique & Florens Jean-Pierre, 2017. "Additive Nonparametric Instrumental Regressions: A Guide to Implementation," Journal of Econometric Methods, De Gruyter, vol. 6(1), pages 1-25, January.
    15. Yongsung Chang & Jay Hong & Marios Karabarbounis & Yicheng Wang & Tao Zhang, 2022. "Income Volatility and Portfolio Choices," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 44, pages 65-90, April.
    16. Costas Meghir & Frank Windmeijer, 1999. "Moment Conditions for Dynamic Panel Data Models with Multiplicative Individual Effects in the Conditional Variance," Annals of Economics and Statistics, GENES, issue 55-56, pages 317-330.
    17. Manuel Arellano & Stéphane Bonhomme, 2012. "Identifying Distributional Characteristics in Random Coefficients Panel Data Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 79(3), pages 987-1020.
    18. Solomon W. Polachek & Tirthatanmoy Das & Rewat Thamma-Apiroam, 2015. "Micro- and Macroeconomic Implications of Heterogeneity in the Production of Human Capital," Journal of Political Economy, University of Chicago Press, vol. 123(6), pages 1410-1455.
    19. Hoderlein, Stefan & Nesheim, Lars & Simoni, Anna, 2017. "Semiparametric Estimation Of Random Coefficients In Structural Economic Models," Econometric Theory, Cambridge University Press, vol. 33(6), pages 1265-1305, December.
    20. Daniel Wilhelm, 2015. "Identification and estimation of nonparametric panel data regressions with measurement error," CeMMAP working papers 34/15, Institute for Fiscal Studies.
    21. Yingyao Hu & Geert Ridder, 2010. "On Deconvolution as a First Stage Nonparametric Estimator," Econometric Reviews, Taylor & Francis Journals, vol. 29(4), pages 365-396.
    22. Geweke, John & Keane, Michael, 2000. "An empirical analysis of earnings dynamics among men in the PSID: 1968-1989," Journal of Econometrics, Elsevier, vol. 96(2), pages 293-356, June.
    23. Fatih Guvenen & Serdar Ozkan & Jae Song, 2014. "The Nature of Countercyclical Income Risk," Journal of Political Economy, University of Chicago Press, vol. 122(3), pages 621-660.
    24. Shane T. Jensen & Stephen H. Shore, 2015. "Changes in the Distribution of Earnings Volatility," Journal of Human Resources, University of Wisconsin Press, vol. 50(3), pages 811-836.
    25. Schennach, Susanne M., 2019. "Convolution without independence," Journal of Econometrics, Elsevier, vol. 211(1), pages 308-318.
    26. Manuel Arellano & Richard Blundell & Stéphane Bonhomme, 2017. "Earnings and Consumption Dynamics: A Nonlinear Panel Data Framework," Econometrica, Econometric Society, vol. 85, pages 693-734, May.
    27. repec:adr:anecst:y:1999:i:55-56:p:12 is not listed on IDEAS
    28. Arellano, Manuel, 2003. "Panel Data Econometrics," OUP Catalogue, Oxford University Press, number 9780199245291.
    29. Xiaohong Chen & Han Hong & Denis Nekipelov, 2011. "Nonlinear Models of Measurement Errors," Journal of Economic Literature, American Economic Association, vol. 49(4), pages 901-937, December.
    30. Carrasco, Marine & Florens, Jean-Pierre, 2011. "A Spectral Method For Deconvolving A Density," Econometric Theory, Cambridge University Press, vol. 27(3), pages 546-581, June.
    31. Evdokimov, Kirill & White, Halbert, 2012. "Some Extensions Of A Lemma Of Kotlarski," Econometric Theory, Cambridge University Press, vol. 28(4), pages 925-932, August.
    32. Costas Meghir & Luigi Pistaferri, 2004. "Income Variance Dynamics and Heterogeneity," Econometrica, Econometric Society, vol. 72(1), pages 1-32, January.
    33. Daniel Wilhelm, 2015. "Identification and estimation of nonparametric panel data regressions with measurement error," CeMMAP working papers CWP34/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    34. Kjetil Storesletten & Chris I. Telmer & Amir Yaron, 2004. "Cyclical Dynamics in Idiosyncratic Labor Market Risk," Journal of Political Economy, University of Chicago Press, vol. 112(3), pages 695-717, June.
    35. Gwo Dong Lin, 2017. "Recent developments on the moment problem," Journal of Statistical Distributions and Applications, Springer, vol. 4(1), pages 1-17, December.
    36. Martin Browning & Mette Ejrnæs, 2013. "Heterogeneity in the Dynamics of Labor Earnings," Annual Review of Economics, Annual Reviews, vol. 5(1), pages 219-245, May.
    37. Gary Chamberlain & Keisuke Hirano, 1999. "Predictive Distributions based on Longitudinal Earnings Data," Annals of Economics and Statistics, GENES, issue 55-56, pages 211-242.
    38. Jiaying Gu & Roger Koenker, 2017. "Unobserved Heterogeneity in Income Dynamics: An Empirical Bayes Perspective," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(1), pages 1-16, January.
    39. Robert A. Moffitt & Peter Gottschalk, 2002. "Trends in the Transitory Variance of Earnings in the United States," Economic Journal, Royal Economic Society, vol. 112(478), pages 68-73, March.
    40. Julie McIntyre & Leonard Stefanski, 2011. "Density Estimation with Replicate Heteroscedastic Measurements," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 63(1), pages 81-99, February.
    41. repec:adr:anecst:y:1999:i:55-56:p:08 is not listed on IDEAS
    42. 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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    More about this item

    Keywords

    Earnings volatility; Panel data; Heteroskedasticity; Linear integral equation;
    All these keywords.

    JEL classification:

    • 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
    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution

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