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

Author

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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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    2. Stefan Boes, 2009. "Bounds on Counterfactual Distributions Under Semi-Monotonicity Constraints," SOI - Working Papers 0920, Socioeconomic Institute - University of Zurich.
    3. 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.
    4. 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.
    5. Le-Yu Chen & Ekaterina Oparina & Nattavudh Powdthavee & Sorawoot Srisuma, 2019. "Robust Ranking of Happiness Outcomes: A Median Regression Perspective," Papers 1902.07696, arXiv.org, revised Apr 2021.
    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, vol. 44(1), pages 81-109, February.
    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. Sukjin Han, 2018. "Identification in Nonparametric Models for Dynamic Treatment Effects," Papers 1805.09397, arXiv.org, revised Jan 2019.
    10. Franco Peracchi & Claudio Rossetti, 2013. "The heterogeneous thresholds ordered response model: identification and inference," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 176(3), pages 703-722, June.
    11. Dionissi Aliprantis, 2017. "Human capital in the inner city," Empirical Economics, Springer, vol. 53(3), pages 1125-1169, November.
    12. 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.
    13. Heckman, James J. & Humphries, John Eric & Veramendi, Gregory, 2016. "Dynamic treatment effects," Journal of Econometrics, Elsevier, vol. 191(2), pages 276-292.
    14. Chen, Le-Yu & Oparina, Ekaterina & Powdthavee, Nattavudh & Srisuma, Sorawoot, 2019. "Have Econometric Analyses of Happiness Data Been Futile? A Simple Truth about Happiness Scales," IZA Discussion Papers 12152, Institute of Labor Economics (IZA).
    15. De los Santos, Babur, 2018. "Consumer search on the Internet," International Journal of Industrial Organization, Elsevier, vol. 58(C), pages 66-105.
    16. 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.
    17. Heckman, James J. & Navarro, Salvador, 2007. "Dynamic discrete choice and dynamic treatment effects," Journal of Econometrics, Elsevier, vol. 136(2), pages 341-396, February.
    18. Philipp Eisenhauer & James J. Heckman & Stefano Mosso, 2015. "Estimation Of Dynamic Discrete Choice Models By Maximum Likelihood And The Simulated Method Of Moments," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 56(2), pages 331-357, May.
    19. Corrado, L. & Weeks, M., 2010. "Identification Strategies in Survey Response Using Vignettes," Cambridge Working Papers in Economics 1031, Faculty of Economics, University of Cambridge.
    20. Jaap H. Abbring, 2010. "Identification of Dynamic Discrete Choice Models," Annual Review of Economics, Annual Reviews, vol. 2(1), pages 367-394, September.
    21. Stefan Boes, 2009. "Partial Identification of Discrete Counterfactual Distributions with Sequential Update of Information," SOI - Working Papers 0918, Socioeconomic Institute - University of Zurich.
    22. 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.

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    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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