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Nonparametric identification of dynamic decision processes with discrete and continuous choices

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  • Jason R. Blevins

Abstract

This paper establishes conditions for nonparametric identification of dynamic optimization models in which agents make both discrete and continuous choices. We consider identification of both the payoff function and the distribution of unobservables. Models of this kind are prevalent in applied microeconomics and many of the required conditions are standard assumptions currently used in empirical work. We focus on conditions on the model that can be implied by economic theory and assumptions about the data generating process that are likely to be satisfied in a typical application. Our analysis is intended to highlight the identifying power of each assumption individually, where possible, and our proofs are constructive in nature.

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  • Jason R. Blevins, 2014. "Nonparametric identification of dynamic decision processes with discrete and continuous choices," Quantitative Economics, Econometric Society, vol. 5(3), pages 531-554, November.
    • Handle: RePEc:wly:quante:v:5:y:2014:i:3:p:531-554
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    Cited by:

    1. Bruneel-Zupanc, Christophe Alain, 2021. "Discrete-Continuous Dynamic Choice Models: Identification and Conditional Choice Probability Estimation," TSE Working Papers 21-1185, Toulouse School of Economics (TSE).
    2. Han, Sukjin, 2021. "Identification in nonparametric models for dynamic treatment effects," Journal of Econometrics, Elsevier, vol. 225(2), pages 132-147.
    3. Myrto Kalouptsidi & Paul T. Scott & Eduardo Souza‐Rodrigues, 2021. "Identification of counterfactuals in dynamic discrete choice models," Quantitative Economics, Econometric Society, vol. 12(2), pages 351-403, May.
    4. Victor Aguirregabiria & Allan Collard-Wexler & Stephen P. Ryan, 2021. "Dynamic Games in Empirical Industrial Organization," NBER Working Papers 29291, National Bureau of Economic Research, Inc.
    5. Heckman, James J. & Humphries, John Eric & Veramendi, Gregory, 2016. "Dynamic treatment effects," Journal of Econometrics, Elsevier, vol. 191(2), pages 276-292.
    6. James J. Heckman & John Eric Humphries & Gregory Veramendi, 2018. "Returns to Education: The Causal Effects of Education on Earnings, Health, and Smoking," Journal of Political Economy, University of Chicago Press, vol. 126(S1), pages 197-246.
    7. Kalouptsidi, Myrto & Scott, Paul T. & Souza-Rodrigues, Eduardo, 2021. "Linear IV regression estimators for structural dynamic discrete choice models," Journal of Econometrics, Elsevier, vol. 222(1), pages 778-804.
    8. James J. Heckman & Rodrigo Pinto, 2018. "Unordered Monotonicity," Econometrica, Econometric Society, vol. 86(1), pages 1-35, January.
    9. Cheng Chou & Geert Ridder & Ruoyao Shi, 2024. "Identification and Estimation of Nonstationary Dynamic Binary Choice Models," Working Papers 202402, University of California at Riverside, Department of Economics.
    10. Sebastian Galiani & Juan Pantano, 2021. "Structural Models: Inception and Frontier," NBER Working Papers 28698, National Bureau of Economic Research, Inc.
    11. Kalouptsidi, Myrto & Souza-Rodrigues, Eduardo & Scott, Paul, 2017. "Identification of Counterfactuals in Dynamic Discrete Choice Models," CEPR Discussion Papers 12470, C.E.P.R. Discussion Papers.
    12. Jason R. Blevins, 2015. "Structural Estimation Of Sequential Games Of Complete Information," Economic Inquiry, Western Economic Association International, vol. 53(2), pages 791-811, April.
    13. Timothy M. Christensen, 2020. "Existence and uniqueness of recursive utilities without boundedness," Papers 2008.00963, arXiv.org, revised Aug 2021.
    14. Timothy M. Christensen, 2018. "Dynamic Models with Robust Decision Makers: Identification and Estimation," Papers 1812.11246, arXiv.org, revised Jan 2019.
    15. Buchholz, Nicholas & Shum, Matthew & Xu, Haiqing, 2021. "Semiparametric estimation of dynamic discrete choice models," Journal of Econometrics, Elsevier, vol. 223(2), pages 312-327.
    16. Schiraldi, Pasquale & Levy, Matthew R., 2020. "Identification of intertemporal preferences in history-dependent dynamic discrete choice models," CEPR Discussion Papers 14447, C.E.P.R. Discussion Papers.
    17. Arcidiacono, Peter & Miller, Robert A., 2020. "Identifying dynamic discrete choice models off short panels," Journal of Econometrics, Elsevier, vol. 215(2), pages 473-485.
    18. Kalouptsidi, Myrto & Scott, Paul T. & Souza-Rodrigues, Eduardo, 2018. "Linear IV Regression Estimators for Structural Dynamic Discrete Choice Models," CEPR Discussion Papers 13240, C.E.P.R. Discussion Papers.
    19. Myrto Kalouptsidi & Paul T. Scott & Eduardo Souza-Rodrigues, 2018. "Linear IV Regression Estimators for Structural Dynamic Discrete Choice Models," NBER Working Papers 25134, National Bureau of Economic Research, Inc.
    20. Otero, Karina V., 2016. "Nonparametric identification of dynamic multinomial choice games: unknown payoffs and shocks without interchangeability," MPRA Paper 86784, University Library of Munich, Germany.
    21. Schiraldi, Pasquale & Levy, Matthew R., 2021. "Identification of Dynamic Discrete-Continuous Choice Models, with an Application to Consumption-Savings-Retirement," CEPR Discussion Papers 15719, C.E.P.R. Discussion Papers.

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