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IV models of ordered choice

Author

Listed:
  • Andrew Chesher

    (Institute for Fiscal Studies and University College London)

  • Konrad Smolinski

    (Institute for Fiscal Studies)

Abstract

This paper studies single equation instrumental variable models of ordered choice in which explanatory variables may be endogenous. The models are weakly restrictive, leaving unspecified the mechanism that generates endogenous variables. These incomplete models are set, not point, identifying for parametrically (e.g. ordered probit) or nonparametrically specified structural functions. The paper gives results on the properties of the identified set for the case in which potentially endogenous explanatory variables are discrete. The results are used as the basis for calculations showing the rate of shrinkage of identified sets as the number of classes in which the outcome is categorised increases.

Suggested Citation

  • Andrew Chesher & Konrad Smolinski, 2009. "IV models of ordered choice," CeMMAP working papers CWP37/09, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    • Handle: RePEc:ifs:cemmap:37/09
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    File URL: http://cemmap.ifs.org.uk/wps/cwp3709.pdf
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    Cited by:

    1. Zhou Xun, 2015. "Preference for Redistribution and Inequality Perception in China: Evidence from the CGSS 2006," Working Papers halshs-01143131, HAL.
    2. Ian Walker & Rhys Wheeler & Rob Pryce, 2017. "How much of a problem is problem gambling?," Working Papers 167235280, Lancaster University Management School, Economics Department.
    3. Andrew Chesher & Konrad Smolinski, 2010. "Sharp identified sets for discrete variable IV models," CeMMAP working papers CWP11/10, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Andrei A. Sirchenko, 2017. "An endogenous regime-switching model of ordered choice with an application to federal funds rate target," 2017 Papers psi424, Job Market Papers.
    5. Andrew Chesher & Adam Rosen & Zahra Siddique, 2019. "Estimating Endogenous Effects on Ordinal Outcomes," CeMMAP working papers CWP66/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    6. Zhou Xun & Michel Lubrano, 2022. "Preference for Redistribution, Poverty Perception among Chinese Migrants," AMSE Working Papers 2228, Aix-Marseille School of Economics, France.
    7. Alexander Torgovitsky, 2019. "Partial identification by extending subdistributions," Quantitative Economics, Econometric Society, vol. 10(1), pages 105-144, January.
    8. Eleni (E.) Aristodemou, 2018. "Semiparametric Identification in Panel Data Discrete Response Models," Tinbergen Institute Discussion Papers 18-065/III, Tinbergen Institute.
    9. Biørn, Erik, 2017. "Identification, Instruments, Omitted Variables, and Rudimentary Models: Fallacies in the ‘Experimental Approach’ to Econometrics," Memorandum 13/2017, Oslo University, Department of Economics.
    10. Thierry Magnac, 2013. "Identification partielle : méthodes et conséquences pour les applications empiriques," L'Actualité Economique, Société Canadienne de Science Economique, vol. 89(4), pages 233-258.
    11. Zhou Xun, 2015. "Preference for Redistribution and Inequality Perception in China: Evidence from the CGSS 2006," AMSE Working Papers 1518, Aix-Marseille School of Economics, France.
    12. Seya, Hajime & Nakamichi, Kumiko & Yamagata, Yoshiki, 2016. "The residential parking rent price elasticity of car ownership in Japan," Transportation Research Part A: Policy and Practice, Elsevier, vol. 85(C), pages 123-134.
    13. Cameron McIntosh, 2014. "The presence of an error term does not preclude causal inference in regression: a comment on Krause (2012)," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(1), pages 243-250, January.
    14. Chesher, Andrew & Rosen, Adam M., 2020. "Generalized instrumental variable models, methods, and applications," Handbook of Econometrics, in: Steven N. Durlauf & Lars Peter Hansen & James J. Heckman & Rosa L. Matzkin (ed.), Handbook of Econometrics, edition 1, volume 7, chapter 0, pages 1-110, Elsevier.
    15. Aradillas-López, Andrés & Rosen, Adam M., 2022. "Inference in ordered response games with complete information," Journal of Econometrics, Elsevier, vol. 226(2), pages 451-476.
    16. Andrew Chesher & Adam M. Rosen, 2017. "Generalized Instrumental Variable Models," Econometrica, Econometric Society, vol. 85, pages 959-989, May.
    17. Chesher, Andrew, 2013. "Semiparametric Structural Models Of Binary Response: Shape Restrictions And Partial Identification," Econometric Theory, Cambridge University Press, vol. 29(2), pages 231-266, April.
    18. Andrews, Donald W.K. & Shi, Xiaoxia, 2017. "Inference based on many conditional moment inequalities," Journal of Econometrics, Elsevier, vol. 196(2), pages 275-287.
    19. Kajal Lahiri & Liu Yang, 2021. "Estimating Endogenous Ordered Response Panel Data Models with an Application to Income Gradient in Child Health," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(2), pages 207-243, November.
    20. Long Hai Vo & Thai-Ha Le & Donghyun Park, 2025. "Digital Divide Decoded: Can E-Commerce and Remote Workforce Enhance Enterprise Resilience in the Covid-19 Era?," Journal of Economic Development, The Economic Research Institute, Chung-Ang University, vol. 50(4), pages 25-57, December.

    More about this item

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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