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IV models of ordered choice

Author

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  • 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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    References listed on IDEAS

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    1. Victor Chernozhukov & Sokbae Lee & Adam M. Rosen, 2013. "Intersection Bounds: Estimation and Inference," Econometrica, Econometric Society, vol. 81(2), pages 667-737, March.
    2. Richard W. Blundell & James L. Powell, 2004. "Endogeneity in Semiparametric Binary Response Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 71(3), pages 655-679.
    3. Smith, Richard J & Blundell, Richard W, 1986. "An Exogeneity Test for a Simultaneous Equation Tobit Model with an Application to Labor Supply," Econometrica, Econometric Society, vol. 54(3), pages 679-685, May.
    4. Heckman, James J, 1978. "Dummy Endogenous Variables in a Simultaneous Equation System," Econometrica, Econometric Society, vol. 46(4), pages 931-959, July.
    5. Andrew Chesher, 2005. "Nonparametric Identification under Discrete Variation," Econometrica, Econometric Society, vol. 73(5), pages 1525-1550, September.
    6. Rivers, Douglas & Vuong, Quang H., 1988. "Limited information estimators and exogeneity tests for simultaneous probit models," Journal of Econometrics, Elsevier, vol. 39(3), pages 347-366, November.
    7. Andrew Chesher, 2003. "Identification in Nonseparable Models," Econometrica, Econometric Society, vol. 71(5), pages 1405-1441, September.
    8. Andrew Chesher, 2010. "Instrumental Variable Models for Discrete Outcomes," Econometrica, Econometric Society, vol. 78(2), pages 575-601, March.
    9. Victor Chernozhukov & Christian Hansen, 2005. "An IV Model of Quantile Treatment Effects," Econometrica, Econometric Society, vol. 73(1), pages 245-261, January.
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    Citations

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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. 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.
    3. Rob Pryce & Ian Walker & Rhys Wheeler, 2017. "How much of a problem is problem gambling?," Working Papers 167235280, Lancaster University Management School, Economics Department.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. Zhou Xun & Michel Lubrano, 2022. "Preference for Redistribution, Poverty Perception among Chinese Migrants," Working Papers hal-03886239, HAL.
    9. Alexander Torgovitsky, 2019. "Partial identification by extending subdistributions," Quantitative Economics, Econometric Society, vol. 10(1), pages 105-144, January.
    10. Eleni (E.) Aristodemou, 2018. "Semiparametric Identification in Panel Data Discrete Response Models," Tinbergen Institute Discussion Papers 18-065/III, Tinbergen Institute.
    11. Andrew Chesher & Adam M. Rosen, 2017. "Generalized Instrumental Variable Models," Econometrica, Econometric Society, vol. 85, pages 959-989, May.
    12. 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.
    13. Magnac, Thierry, 2013. "Identification partielle : méthodes et conséquences pour les applications empiriques," L'Actualité Economique, Société Canadienne de Science Economique, vol. 89(4), pages 233-258, Décembre.
    14. 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.
    15. 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.
    16. 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.
    17. Andrews, Donald W.K. & Shi, Xiaoxia, 2017. "Inference based on many conditional moment inequalities," Journal of Econometrics, Elsevier, vol. 196(2), pages 275-287.
    18. 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.
    19. Vo, Long Hai & Le, Thai-Ha & Park, Donghyun, 2022. "Digital Divide Decoded: Can E-Commerce and Remote Workforce Enhance Enterprise Resilience in the COVID-19 Era?," ADB Economics Working Paper Series 667, Asian Development Bank.

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    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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