IDEAS home Printed from https://ideas.repec.org/a/wly/quante/v5y2014i3p531-554.html
   My bibliography  Save this article

Nonparametric identification of dynamic decision processes with discrete and continuous choices

Author

Listed:
  • 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.

Suggested Citation

  • 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
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. Sukjin Han, 2018. "Identification in Nonparametric Models for Dynamic Treatment Effects," Papers 1805.09397, arXiv.org, revised Jan 2019.
    3. Myrto Kalouptsidi & Paul T. Scott & Eduardo Souza‚ÄźRodrigues, 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. Han, Sukjin, 2021. "Identification in nonparametric models for dynamic treatment effects," Journal of Econometrics, Elsevier, vol. 225(2), pages 132-147.
    7. 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.
    8. 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.
    9. James J. Heckman & Rodrigo Pinto, 2018. "Unordered Monotonicity," Econometrica, Econometric Society, vol. 86(1), pages 1-35, January.
    10. Sebastian Galiani & Juan Pantano, 2021. "Structural Models: Inception and Frontier," NBER Working Papers 28698, National Bureau of Economic Research, Inc.
    11. Kalouptsidi, Myrto & Scott, Paul & Souza-Rodrigues, Eduardo, 2017. "Identification of Counterfactuals in Dynamic Discrete Choice Models," CEPR Discussion Papers 12470, C.E.P.R. Discussion Papers.
    12. Timothy M. Christensen, 2020. "Existence and uniqueness of recursive utilities without boundedness," Papers 2008.00963, arXiv.org, revised Aug 2021.
    13. Timothy M. Christensen, 2018. "Dynamic Models with Robust Decision Makers: Identification and Estimation," Papers 1812.11246, arXiv.org, revised Jan 2019.
    14. Buchholz, Nicholas & Shum, Matthew & Xu, Haiqing, 2021. "Semiparametric estimation of dynamic discrete choice models," Journal of Econometrics, Elsevier, vol. 223(2), pages 312-327.
    15. Levy, Matthew & Schiraldi, Pasquale, 2020. "Identification of intertemporal preferences in history-dependent dynamic discrete choice models," CEPR Discussion Papers 14447, C.E.P.R. Discussion Papers.
    16. Arcidiacono, Peter & Miller, Robert A., 2020. "Identifying dynamic discrete choice models off short panels," Journal of Econometrics, Elsevier, vol. 215(2), pages 473-485.
    17. 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.
    18. 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.
    19. 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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:quante:v:5:y:2014:i:3:p:531-554. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: https://edirc.repec.org/data/essssea.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/essssea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.