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Simple Estimation of a Duration Model with Unobserved Heterogeneity

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  • Honore, Bo E
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    Abstract

    This paper presents a simple estimator of the shape parameter in a Weibull duration model with unobserved heterogeneity. The estimator is consistent and asymptotically normal under mild conditions, and a consistent estimator of the asymptotic variance is available. A Monte Carlo study indicates that the asymptotic distribution of the estimator provides a good approximation to the finite sample distribution. The estimation strategy can be extended to a model with regressors and to a log-logistic model with unobserved heterogeneity. The advantages of the estimator are that it is easy to calculate and that its asymptotic distribution can be derived. Copyright 1990 by The Econometric Society.

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    Bibliographic Info

    Article provided by Econometric Society in its journal Econometrica.

    Volume (Year): 58 (1990)
    Issue (Month): 2 (March)
    Pages: 453-73

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    Handle: RePEc:ecm:emetrp:v:58:y:1990:i:2:p:453-73

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    Cited by:
    1. Jin Seo Cho & Halbert White, 2009. "Testing for Unobserved Heterogeneity in Exponential and Weibull Duration Models," Discussion Paper Series 0912, Institute of Economic Research, Korea University.
    2. Ayala, Luis & Rodriguez, Magdalena, 2007. "Barriers to employment and welfare dynamics: Evidence from Spain," Journal of Policy Modeling, Elsevier, vol. 29(2), pages 237-257.
    3. James J. Heckman & Christopher R. Taber, 1994. "Econometric Mixture Models and More General Models for Unobservables in Duration Analysis," NBER Technical Working Papers 0157, National Bureau of Economic Research, Inc.
    4. Tiemen Woutersen & Jerry Hausman, 2005. "Estimating a Semi-Parametric Duration Model without Specifying Heterogeneity," Economics Working Paper Archive 525, The Johns Hopkins University,Department of Economics.
    5. Hilary W. Hoynes & Kenneth Y. Chay & Dean Hyslop, 2004. "True State Dependence In Monthly Welfare Participation:A Nonexperimental Analysis," Working Papers 533, University of California, Davis, Department of Economics.
    6. Bo E. Honor & �ureo De Paula, 2010. "Interdependent Durations," Review of Economic Studies, Oxford University Press, vol. 77(3), pages 1138-1163.
    7. Govert Bijwaard & Geert Ridder & Tiemen Woutersen, 2012. "A Simple GMM Estimator for the Semiparametric Mixed Proportional Hazard Model," Norface Discussion Paper Series 2012035, Norface Research Programme on Migration, Department of Economics, University College London.
    8. Nikolaus Hautsch, 1999. "Analyzing the Time between Trades with a Gamma Compounded Hazard Model. An Application to LIFFE Bund Future Transactions," CoFE Discussion Paper 99-03, Center of Finance and Econometrics, University of Konstanz.
    9. Imbens, Guido W. & Lynch, Lisa M., 2006. "Re-Employment Probabilities over the Business Cycle," IZA Discussion Papers 2167, Institute for the Study of Labor (IZA).
    10. Gerhard, Frank & Hautsch, Nikolaus, 2002. "Volatility estimation on the basis of price intensities," Journal of Empirical Finance, Elsevier, vol. 9(1), pages 57-89, January.
    11. Hausman, Jerry A. & Woutersen, Tiemen, 2014. "Estimating a semi-parametric duration model without specifying heterogeneity," Journal of Econometrics, Elsevier, vol. 178(P1), pages 114-131.
    12. Bo E. Honoré & Aureo de Paula, 2009. ""Interdependent Durations" Third Version," PIER Working Paper Archive 09-039, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 01 Feb 2008.
    13. Lee, Sokbae, 2008. "Estimating Panel Data Duration Models With Censored Data," Econometric Theory, Cambridge University Press, vol. 24(05), pages 1254-1276, October.
    14. Bo E. Honore & Aureo de Paula, 2007. "Interdependent Durations, Second Version," PIER Working Paper Archive 08-044, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 01 Nov 2008.

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