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Instrumental Variable Estimation for Duration Data

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  • Govert E. Bijwaard

    ()
    (Erasmus University Rotterdam)

Abstract

In this article we develop an Instrumental Variable estimation procedure that corrects for possible endogeneity of a variable in a duration model. We assume a Generalized Accelerated Failure Time (GAFT) model. This model is based on transforming the durations and assuming a distribution for these transformed durations. The GAFT model encompasses two competing approaches to duration data; the (Mixed) Proportional Hazard (MPH) model and the Accelerated Failure Time (AFT) model. The basis of the Instrumental Variable Linear Rank estimator (IVLR) is that for the true GAFT model the instrument does not influence the hazard of the transformed duration. The inverse of an extended rank test provide the estimation equations the IVLR estimation procedure is based on. We discuss the large sample properties and the efficiency of this estimator. We discuss the practical issues of implementation of the estimator. We apply the IVLR estimation approach to the Illinois re-employment bonus experiment. In this experiment individuals who became unemployed were divided at random in three groups: two bonus groups and a control group. Those in the bonus groups could refuse to participate in the experiment. It is very likely that this decision is related to the unemployment duration. We use the IVLR estimator to obtain the effect of these endogenous claimant and employer bonuses on the re-employment hazard.

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Bibliographic Info

Paper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 08-032/4.

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Date of creation: 27 Mar 2008
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Handle: RePEc:dgr:uvatin:20080032

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Web page: http://www.tinbergen.nl

Related research

Keywords: Endogenous Variable; Duration model; Censoring; Instrumental Variable;

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References

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  1. Joshua Angrist & Alan Krueger, 1998. "Empirical Strategies in Labor Economics," Working Papers 780, Princeton University, Department of Economics, Industrial Relations Section..
  2. Van den Berg, Gerard J., 2000. "Duration Models: Specification, Identification, and Multiple Durations," MPRA Paper 9446, University Library of Munich, Germany.
  3. Jaap H. Abbring & Gerard J. van den Berg, 2005. "Social Experiments and Instrumental Variables with Duration Outcomes," Tinbergen Institute Discussion Papers 05-047/3, Tinbergen Institute.
  4. Moffitt, Robert, 1983. "An Economic Model of Welfare Stigma," American Economic Review, American Economic Association, vol. 73(5), pages 1023-35, December.
  5. Govert Bijwaard & Geert Ridder, 1998. "Correcting for Selective Compliance in a Re-Employment Bonus Experiment," Economics Working Paper Archive 412, The Johns Hopkins University,Department of Economics.
  6. Han, Aaron K., 1987. "Non-parametric analysis of a generalized regression model : The maximum rank correlation estimator," Journal of Econometrics, Elsevier, vol. 35(2-3), pages 303-316, July.
  7. Bruce D. Meyer, 1995. "Lessons from the U.S. Unemployment Insurance Experiments," Journal of Economic Literature, American Economic Association, vol. 33(1), pages 91-131, March.
  8. Lalive, Rafael & van Ours, Jan C. & Zweimüller, Josef, 2002. "The Effect of Benefit Sanctions on the Duration of Unemployment," IZA Discussion Papers 469, Institute for the Study of Labor (IZA).
  9. Gerard J. van den Berg & Bas van der Klaauw & Jan C. van Ours, 1998. "Punitive Sanctions and the Transition Rate from Welfare to Work," Tinbergen Institute Discussion Papers 98-076/3, Tinbergen Institute.
  10. Ashenfelter, Orley & Ashmore, David & Deschenes, Olivier, 2000. "Do Unemployment Insurance Recipients Actively Seek Work? Evidence From Randomized Trials in Four U.S. States," IZA Discussion Papers 128, Institute for the Study of Labor (IZA).
  11. Geert Ridder & Tiemen Woutersen, 2001. "The Singularity of the Efficiency Bound of the Mixed Proportional Hazard Model," UWO Department of Economics Working Papers 20019, University of Western Ontario, Department of Economics.
  12. repec:dgr:uvatin:2098076 is not listed on IDEAS
  13. Yannis Bilias & Roger Koenker, 2001. "Quantile regression for duration data: A reappraisal of the Pennsylvania Reemployment Bonus Experiments," Empirical Economics, Springer, vol. 26(1), pages 199-220.
  14. Gerard J. van den Berg & Bas van der Klaauw & Jan C. van Ours, 2004. "Punitive Sanctions and the Transition Rate from Welfare to Work," Journal of Labor Economics, University of Chicago Press, vol. 22(1), pages 211-241, January.
  15. Meyer, Bruce D, 1996. "What Have We Learned from the Illinois Reemployment Bonus Experiment?," Journal of Labor Economics, University of Chicago Press, vol. 14(1), pages 26-51, January.
  16. Ridder, Geert, 1990. "The Non-parametric Identification of Generalized Accelerated Failure-Time Models," Review of Economic Studies, Wiley Blackwell, vol. 57(2), pages 167-81, April.
  17. Woodbury, Stephen A & Spiegelman, Robert G, 1987. "Bonuses to Workers and Employers to Reduce Unemployment: Randomized Trials in Illinois," American Economic Review, American Economic Association, vol. 77(4), pages 513-30, September.
  18. Koenker R. & Geling O., 2001. "Reappraising Medfly Longevity: A Quantile Regression Survival Analysis," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 458-468, June.
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Citations

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Cited by:
  1. Abbring, Jaap H & van den Berg, Gerard J, 2005. "Social experiments and intrumental variables with duration outcomes," Working Paper Series 2005:11, IFAU - Institute for Evaluation of Labour Market and Education Policy.
  2. Bijwaard, Govert & Ridder, Geert, 2009. "A Simple GMM Estimator for the Semi-Parametric Mixed Proportional Hazard Model," IZA Discussion Papers 4543, Institute for the Study of Labor (IZA).
  3. Spierdijk, Laura & van Lomwel, Gijsbert & Peppelman, Wilko, 2009. "The determinants of sick leave durations of Dutch self-employed," Journal of Health Economics, Elsevier, vol. 28(6), pages 1185-1196, December.
  4. Kim Economides & Alfred A. Haug & Joe McIntyre, 2013. "Are Courts Slow? Exposing and Measuring the Invisible Determinants of Case Disposition Time," Working Papers 1317, University of Otago, Department of Economics, revised Nov 2013.

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