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The Identification And Economic Content Of Ordered Choice Models With Stochastic Thresholds

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  • Flavio Cunha
  • James J. Heckman
  • Salvador Navarro

Abstract

This article extends the widely used ordered choice model by introducing stochastic thresholds and interval-specific outcomes. The model can be interpreted as a generalization of the GAFT (MPH) framework for discrete duration data that jointly models durations and outcomes associated with different stopping times. We establish conditions for nonparametric identification. We interpret the ordered choice model as a special case of a general discrete choice model and as a special case of a dynamic discrete choice model. Copyright 2007 by the Economics Department Of The University Of Pennsylvania And Osaka University Institute Of Social And Economic Research Association.

Suggested Citation

  • Flavio Cunha & James J. Heckman & Salvador Navarro, 2007. "The Identification And Economic Content Of Ordered Choice Models With Stochastic Thresholds," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 48(4), pages 1273-1309, November.
  • Handle: RePEc:ier:iecrev:v:48:y:2007:i:4:p:1273-1309
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    References listed on IDEAS

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    4. Heckman, James J. & Navarro, Salvador, 2007. "Dynamic discrete choice and dynamic treatment effects," Journal of Econometrics, Elsevier, pages 341-396.
    5. James J. Heckman, 1974. "Effects of Child-Care Programs on Women's Work Effort," NBER Chapters,in: Marriage, Family, Human Capital, and Fertility, pages 136-169 National Bureau of Economic Research, Inc.
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    9. James J. Heckman & Vytlacil, Edward J., 2007. "Econometric Evaluation of Social Programs, Part I: Causal Models, Structural Models and Econometric Policy Evaluation," Handbook of Econometrics,in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 70 Elsevier.
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    12. Abbring, Jaap H. & Heckman, James J., 2007. "Econometric Evaluation of Social Programs, Part III: Distributional Treatment Effects, Dynamic Treatment Effects, Dynamic Discrete Choice, and General Equilibrium Policy Evaluation," Handbook of Econometrics,in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 72 Elsevier.
    13. Martin Feldstein, 1983. "Behavioral Simulation Methods in Tax Policy Analysis," NBER Books, National Bureau of Economic Research, Inc, number feld83-2, January.
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    Citations

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    Cited by:

    1. Heckman, James J. & Navarro, Salvador, 2007. "Dynamic discrete choice and dynamic treatment effects," Journal of Econometrics, Elsevier, pages 341-396.
    2. Heckman, James J. & Urzúa, Sergio, 2010. "Comparing IV with structural models: What simple IV can and cannot identify," Journal of Econometrics, Elsevier, vol. 156(1), pages 27-37, May.
    3. Stefan Boes, 2009. "Bounds on Counterfactual Distributions Under Semi-Monotonicity Constraints," SOI - Working Papers 0920, Socioeconomic Institute - University of Zurich.
    4. Timothy A. Weterings & Mark N. Harris & Bruce Hollingsworth, 2012. "Extending Unobserved Heterogeneity - A Strategy for Accounting for Respondent Perceptions in the Absence of Suitable Data," Monash Econometrics and Business Statistics Working Papers 12/12, Monash University, Department of Econometrics and Business Statistics.
    5. Powdthavee, Nattavudh, 2009. "Ill-health as a household norm: Evidence from other people's health problems," Social Science & Medicine, Elsevier, vol. 68(2), pages 251-259, January.
    6. Jaap Abbring & James Heckman, 2008. "Dynamic policy analysis," CeMMAP working papers CWP05/08, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    7. Stefan Boes, 2013. "Nonparametric analysis of treatment effects in ordered response models," Empirical Economics, Springer, pages 81-109.
    8. James J. Heckman & Sergio Urzua & Edward Vytlacil, 2006. "Understanding Instrumental Variables in Models with Essential Heterogeneity," The Review of Economics and Statistics, MIT Press, vol. 88(3), pages 389-432, August.
    9. Corrado, L. & Weeks, M., 2010. "Identification Strategies in Survey Response Using Vignettes," Cambridge Working Papers in Economics 1031, Faculty of Economics, University of Cambridge.
    10. Jaap H. Abbring, 2010. "Identification of Dynamic Discrete Choice Models," Annual Review of Economics, Annual Reviews, vol. 2(1), pages 367-394, September.
    11. repec:spr:empeco:v:53:y:2017:i:3:d:10.1007_s00181-016-1160-y is not listed on IDEAS
    12. Stefan Boes, 2009. "Partial Identification of Discrete Counterfactual Distributions with Sequential Update of Information," SOI - Working Papers 0918, Socioeconomic Institute - University of Zurich.
    13. Jane Cooley Fruehwirth & Salvador Navarro & Yuya Takahashi, 2016. "How the Timing of Grade Retention Affects Outcomes: Identification and Estimation of Time-Varying Treatment Effects," Journal of Labor Economics, University of Chicago Press, vol. 34(4), pages 979-1021.
    14. Juliet Elu & Gregory Price, 2015. "Consumer’s Surplus with a Racial Apology? Black Relative to Non-Black Inequality in the Welfare Gains of Fuel-Efficient Cars and Trucks," The Review of Black Political Economy, Springer;National Economic Association, vol. 42(1), pages 135-154, June.
    15. Heckman, James J. & Humphries, John Eric & Veramendi, Gregory, 2016. "Dynamic treatment effects," Journal of Econometrics, Elsevier, vol. 191(2), pages 276-292.

    More about this item

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models

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