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The Dynamics of Depression from Adolescence to Early Adulthood

Author

Listed:
  • Paul Contoyannis
  • Jinhu Li
Abstract
This paper employs a conditional quantile regression approach to quantify the dynamics of depression among adolescents, and examine the extent of true state dependence in youth depression conditional on unobserved individual heterogeneity and family socio5economic status. We use data on the children of the US National Longitudinal Survey of Youth 79 cohort (CNLSY79) and employ a recently5developed instrumental variable approach for the estimation of dynamic quantile regression models with fixed effects. Our results suggest that true state dependence in youth depression is very low and the observed positive association between previous depression and current depression is mainly due to time5invariant unobserved individual heterogeneity. The results also show heterogeneity in true state dependence in youth depression across quantiles of the depression distribution.

Suggested Citation

  • Paul Contoyannis & Jinhu Li, 2014. "The Dynamics of Depression from Adolescence to Early Adulthood," Department of Economics Working Papers 2014-09, McMaster University.
    • Handle: RePEc:mcm:deptwp:2014-09
    as

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    File URL: http://socserv.mcmaster.ca/econ/rsrch/papers/archive/2014-09.pdf
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    References listed on IDEAS

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    1. James P. Smith, 2009. "The Impact of Childhood Health on Adult Labor Market Outcomes," The Review of Economics and Statistics, MIT Press, vol. 91(3), pages 478-489, August.
    2. Koenker, Roger, 2004. "Quantile regression for longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 91(1), pages 74-89, October.
    3. Andrew M. Jones & Nigel Rice & Paul Contoyannis, 2012. "The Dynamics of Health," Chapters, in: Andrew M. Jones (ed.), The Elgar Companion to Health Economics, Second Edition, chapter 2, Edward Elgar Publishing.
    4. Andrew M. Jones (ed.), 2006. "The Elgar Companion to Health Economics," Books, Edward Elgar Publishing, number 3572.
    5. Smith, James Patrick & Smith, Gillian C., 2010. "Long-term economic costs of psychological problems during childhood," Social Science & Medicine, Elsevier, vol. 71(1), pages 110-115, July.
    6. Machado, Jose A.F. & Silva, J. M. C. Santos, 2005. "Quantiles for Counts," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1226-1237, December.
    7. Rosen, Sherwin, 2007. "Studies in Labor Markets," National Bureau of Economic Research Books, University of Chicago Press, number 9780226726304.
    8. Chernozhukov, Victor & Hansen, Christian, 2008. "Instrumental variable quantile regression: A robust inference approach," Journal of Econometrics, Elsevier, vol. 142(1), pages 379-398, January.
    9. John Roy & Stefanie Schurer, 2013. "Getting Stuck In The Blues: Persistence Of Mental Health Problems In Australia," Health Economics, John Wiley & Sons, Ltd., vol. 22(9), pages 1139-1157, September.
    10. Chernozhukov, Victor & Hansen, Christian, 2006. "Instrumental quantile regression inference for structural and treatment effect models," Journal of Econometrics, Elsevier, vol. 132(2), pages 491-525, June.
    11. Anderson, T. W. & Hsiao, Cheng, 1982. "Formulation and estimation of dynamic models using panel data," Journal of Econometrics, Elsevier, vol. 18(1), pages 47-82, January.
    12. Galvao Jr., Antonio F., 2011. "Quantile regression for dynamic panel data with fixed effects," Journal of Econometrics, Elsevier, vol. 164(1), pages 142-157, September.
    13. Dean R. Hyslop, 1999. "State Dependence, Serial Correlation and Heterogeneity in Intertemporal Labor Force Participation of Married Women," Econometrica, Econometric Society, vol. 67(6), pages 1255-1294, November.
    14. Katharina Hauck & Nigel Rice, 2004. "A longitudinal analysis of mental health mobility in Britain," Health Economics, John Wiley & Sons, Ltd., vol. 13(10), pages 981-1001, October.
    15. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 277-297.
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    More about this item

    Keywords

    adolescence; depression; dynamic quantile regressions; health dynamics; instrumental variables; quantile regression; panel data models;
    All these keywords.

    JEL classification:

    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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