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Semiparametric competing risks analysis

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  • José Canals-Cerdá
  • Shiferaw Gurmu

Abstract

In this paper we analyse a semi-parametric estimation technique for competing risks models based on series expansion of the joint density of the unobserved heterogeneity components. This technique allows for unrestricted correlation among the risks. The finite sample behavior of the estimation technique is analysed in a Monte Carlo experiment using an empirically relevant data-generating process. The estimator performs well when compared with the Heckman--Singer estimator. Copyright Royal Economic Society 2007

Suggested Citation

  • José Canals-Cerdá & Shiferaw Gurmu, 2007. "Semiparametric competing risks analysis," Econometrics Journal, Royal Economic Society, vol. 10(2), pages 193-215, July.
    • Handle: RePEc:ect:emjrnl:v:10:y:2007:i:2:p:193-215
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    Cited by:

    1. Piu Banerjee & Jose J. Canals-Cerda, 2012. "Credit risk analysis of credit card portfolios under economic stress conditions," Working Papers 12-18, Federal Reserve Bank of Philadelphia.
    2. James Marton & Patricia G. Ketsche & Mei Zhou, 2010. "SCHIP premiums, enrollment, and expenditures: a two state, competing risk analysis," Health Economics, John Wiley & Sons, Ltd., vol. 19(7), pages 772-791, July.
    3. Subhasree Basu Roy, 2018. "Effect of Health on Retirement of Older Americans: a Competing Risks Study," Journal of Labor Research, Springer, vol. 39(1), pages 56-98, March.
    4. José Canals-Cerdá & Shiferaw Gurmu, 2008. "Premarital Birth Among Young Hispanic Women: Evidence from Semiparametric Competing Risks Analysis," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 36(4), pages 421-440, December.

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