IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2406.07809.html
   My bibliography  Save this paper

Did Harold Zuercher Have Time-Separable Preferences?

Author

Listed:
  • Jay Lu
  • Yao Luo
  • Kota Saito
  • Yi Xin

Abstract

This paper proposes an empirical model of dynamic discrete choice to allow for non-separable time preferences, generalizing the well-known Rust (1987) model. Under weak conditions, we show the existence of value functions and hence well-defined optimal choices. We construct a contraction mapping of the value function and propose an estimation method similar to Rust's nested fixed point algorithm. Finally, we apply the framework to the bus engine replacement data. We improve the fit of the data with our general model and reject the null hypothesis that Harold Zuercher has separable time preferences. Misspecifying an agent's preference as time-separable when it is not leads to biased inferences about structure parameters (such as the agent's risk attitudes) and misleading policy recommendations.

Suggested Citation

  • Jay Lu & Yao Luo & Kota Saito & Yi Xin, 2024. "Did Harold Zuercher Have Time-Separable Preferences?," Papers 2406.07809, arXiv.org.
    • Handle: RePEc:arx:papers:2406.07809
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2406.07809
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Larry G. Epstein & Emmanuel Farhi & Tomasz Strzalecki, 2014. "How Much Would You Pay to Resolve Long-Run Risk?," American Economic Review, American Economic Association, vol. 104(9), pages 2680-2697, September.
    2. Victor Aguirregabiria & Pedro Mira, 2002. "Swapping the Nested Fixed Point Algorithm: A Class of Estimators for Discrete Markov Decision Models," Econometrica, Econometric Society, vol. 70(4), pages 1519-1543, July.
    3. Hanming Fang & Yang Wang, 2015. "Estimating Dynamic Discrete Choice Models With Hyperbolic Discounting, With An Application To Mammography Decisions," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 56(2), pages 565-596, May.
    4. Hanming Fang & Yang Wang, 2015. "Estimating Dynamic Discrete Choice Models With Hyperbolic Discounting, With An Application To Mammography Decisions," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 56, pages 565-596, May.
    5. Lorenzo Stanca, 2023. "Recursive Preferences, Correlation Aversion, and the Temporal Resolution of Uncertainty," Carlo Alberto Notebooks 693 JEL Classification: C, Collegio Carlo Alberto.
    6. Mitsuru Igami, 2017. "Estimating the Innovator’s Dilemma: Structural Analysis of Creative Destruction in the Hard Disk Drive Industry, 1981–1998," Journal of Political Economy, University of Chicago Press, vol. 125(3), pages 798-847.
    7. Patrick Bajari & Chenghuan Sean Chu & Denis Nekipelov & Minjung Park, 2016. "Identification and semiparametric estimation of a finite horizon dynamic discrete choice model with a terminating action," Quantitative Marketing and Economics (QME), Springer, vol. 14(4), pages 271-323, December.
    8. Lorenzo Maria Stanca, 2023. "Recursive Preferences, Correlation Aversion, and the Temporal Resolution of Uncertainty," Papers 2304.04599, arXiv.org, revised Jul 2023.
    9. Tatiana Komarova & Fabio Sanches & Daniel Silva Junior & Sorawoot Srisuma, 2018. "Joint analysis of the discount factor and payoff parameters in dynamic discrete choice models," Quantitative Economics, Econometric Society, vol. 9(3), pages 1153-1194, November.
    10. Kreps, David M & Porteus, Evan L, 1978. "Temporal Resolution of Uncertainty and Dynamic Choice Theory," Econometrica, Econometric Society, vol. 46(1), pages 185-200, January.
    11. Charalambos D. Aliprantis & Kim C. Border, 2006. "Infinite Dimensional Analysis," Springer Books, Springer, edition 0, number 978-3-540-29587-7, December.
    12. Kyle Handley & J. Frank Li, 2020. "Measuring the Effects of Firm Uncertainty on Economic Activity: New Evidence from One Million Documents," NBER Working Papers 27896, National Bureau of Economic Research, Inc.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Jason R. Blevins & Wei Shi & Donald R. Haurin & Stephanie Moulton, 2020. "A Dynamic Discrete Choice Model Of Reverse Mortgage Borrower Behavior," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 61(4), pages 1437-1477, November.
    3. Jaap H. Abbring & Øystein Daljord, 2020. "Identifying the discount factor in dynamic discrete choice models," Quantitative Economics, Econometric Society, vol. 11(2), pages 471-501, May.
    4. Sebastian Galiani & Juan Pantano, 2021. "Structural Models: Inception and Frontier," NBER Working Papers 28698, National Bureau of Economic Research, Inc.
    5. Mira Frick & Ryota Iijima & Tomasz Strzalecki, 2019. "Dynamic Random Utility," Econometrica, Econometric Society, vol. 87(6), pages 1941-2002, November.
    6. Bloise, Gaetano & Vailakis, Yiannis, 2018. "Convex dynamic programming with (bounded) recursive utility," Journal of Economic Theory, Elsevier, vol. 173(C), pages 118-141.
    7. Fabrice Collard & Sujoy Mukerji & Kevin Sheppard & Jean‐Marc Tallon, 2018. "Ambiguity and the historical equity premium," Quantitative Economics, Econometric Society, vol. 9(2), pages 945-993, July.
    8. Antoine Bommier & Daniel Harenberg & François Le Grand & Cormac O'Dea, 2020. "Recursive Preferences, the Value of Life, and Household Finance," Cowles Foundation Discussion Papers 2231, Cowles Foundation for Research in Economics, Yale University.
    9. Larry G. Epstein & Emmanuel Farhi & Tomasz Strzalecki, 2014. "How Much Would You Pay to Resolve Long-Run Risk?," American Economic Review, American Economic Association, vol. 104(9), pages 2680-2697, September.
    10. Patrick DeJarnette & David Dillenberger & Daniel Gottlieb & Pietro Ortoleva, 2020. "Time Lotteries and Stochastic Impatience," Econometrica, Econometric Society, vol. 88(2), pages 619-656, March.
    11. Rui Albuquerque & Martin Eichenbaum & Victor Xi Luo & Sergio Rebelo, 2016. "Valuation Risk and Asset Pricing," Journal of Finance, American Finance Association, vol. 71(6), pages 2861-2904, December.
    12. Mohammed Abdellaoui & Enrico Diecidue & Emmanuel Kemel & Ayse Onculer, 2022. "Temporal Risk: Utility vs. Probability Weighting," Management Science, INFORMS, vol. 68(7), pages 5162-5186, July.
    13. Mu Zhang, 2021. "A Theory of Choice Bracketing under Risk," Papers 2102.07286, arXiv.org, revised Aug 2021.
    14. Aycinena, Diego & Blazsek, Szabolcs & Rentschler, Lucas & Sprenger, Charles, 2022. "Intertemporal choice experiments and large-stakes behavior," Journal of Economic Behavior & Organization, Elsevier, vol. 196(C), pages 484-500.
    15. Justina Klimaviciute, 2024. "(No) time to be healthy: Optimal policy with time and monetary investments in health," Health Economics, John Wiley & Sons, Ltd., vol. 33(3), pages 410-431, March.
    16. 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.
    17. Kelsall, Claudia & Quaas, Martin F. & Quérou, Nicolas, 2023. "Risk aversion in renewable resource harvesting," Journal of Environmental Economics and Management, Elsevier, vol. 121(C).
    18. AJ A. Bostian & Christoph Heinzel, 2018. "Comparative precautionary saving under higher-order risk and recursive utility," The Geneva Risk and Insurance Review, Palgrave Macmillan;International Association for the Study of Insurance Economics (The Geneva Association), vol. 43(1), pages 95-114, May.
    19. Stan Olijslagers & Sweder van Wijnbergen, 2019. "Discounting the Future: on Climate Change, Ambiguity Aversion and Epstein-Zin Preferences," Tinbergen Institute Discussion Papers 19-030/VI, Tinbergen Institute.
    20. 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.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:arx:papers:2406.07809. See general information about how to correct material in RePEc.

    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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.