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Did Harold Zuercher Have Time-Separable Preferences?

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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
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    References listed on IDEAS

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    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, revised 2025.
    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 Jan 2025.
    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, September.
    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.
    13. Arcidiacono, Peter & Miller, Robert A., 2020. "Identifying dynamic discrete choice models off short panels," Journal of Econometrics, Elsevier, vol. 215(2), pages 473-485.
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