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A Comparison Of Discrete And Parametric Methods For Continuous-State Dynamic Programming Problems

Author

Listed:
  • Hugo Benitez-Silva

    (Yale University)

  • John Rust

    (Yale University)

  • Gunter Hitsch

    (Yale University)

  • Giorgio Pauletto

    (University of Geneva)

  • George Hall

    (Yale University)

Abstract

This paper presents a dynamic model of the joint labor/leisure and consumption/saving decision over the life cycle. Such a dynamic model provides a framework for considering the important policy experiments related to the reforms in Social Security. We address the role of labor supply in a life cyle utility maximization model formally, building upon recent work by Low (1998), and extending the classical optimal lifetime consumption problem under uncertainty first formalized in Phelps (1962) and later in Hakansson (1970). We begin by solving the finite horizon consumption/saving problem analytically and numerically and compare the two solutions. We also simulate this benchmark model. Once the labor choice is considered, the stochastic dynamic programming utility maximization problem of the individual is solved numerically, since analytical solutions are infeasible when the individual is maximizing utility over consumption and leisure, given non-linear marginal utility. We show how such a model captures changes in labor supply over the life cycle and that simulated consumption and wealth accumulation paths are consistent with empirical evidence. We also present a model of endogenously determined annuities in a consumption/saving framework under capital uncertainty and in the presence of bequest motives.

Suggested Citation

  • Hugo Benitez-Silva & John Rust & Gunter Hitsch & Giorgio Pauletto & George Hall, 2000. "A Comparison Of Discrete And Parametric Methods For Continuous-State Dynamic Programming Problems," Computing in Economics and Finance 2000 24, Society for Computational Economics.
  • Handle: RePEc:sce:scecf0:24
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    Cited by:

    1. Guofang Huang & Hong Luo & Jing Xia, 2019. "Invest in Information or Wing It? A Model of Dynamic Pricing with Seller Learning," Management Science, INFORMS, vol. 65(12), pages 5556-5583, December.
    2. Aguirregabiria, Victor & Mira, Pedro, 2010. "Dynamic discrete choice structural models: A survey," Journal of Econometrics, Elsevier, vol. 156(1), pages 38-67, May.
    3. Karun Adusumilli & Dita Eckardt, 2019. "Temporal-Difference estimation of dynamic discrete choice models," Papers 1912.09509, arXiv.org, revised Dec 2022.
    4. Thierry Magnac & Pierre Dubois, 2016. "Consumer Demand with Unobserved Stockpiling and Intertemporal Price Discrimination," 2016 Meeting Papers 451, Society for Economic Dynamics.
    5. George Hall and John Rust, Yale University, 2001. "Econometric Methods for Endogenously Sampled Time Series: The Case of Commodity Price Speculation in the Steel Market," Computing in Economics and Finance 2001 274, Society for Computational Economics.
    6. Aruoba, S. Boragan & Fernandez-Villaverde, Jesus & Rubio-Ramirez, Juan F., 2006. "Comparing solution methods for dynamic equilibrium economies," Journal of Economic Dynamics and Control, Elsevier, vol. 30(12), pages 2477-2508, December.
    7. Mariacristina Rossi & Dario Sansone, 2018. "Precautionary savings and the self-employed," Small Business Economics, Springer, vol. 51(1), pages 105-127, June.
    8. Peter Arcidiacono & Patrick Bayer & Federico A. Bugni & Jonathan James, 2012. "Approximating High-Dimensional Dynamic Models: Sieve Value Function Iteration," NBER Working Papers 17890, National Bureau of Economic Research, Inc.
    9. Fernández-Villaverde, Jesús & Hull, Isaiah, 2023. "Dynamic Programming on a Quantum Annealer: Solving the RBC Model," CEPR Discussion Papers 18190, C.E.P.R. Discussion Papers.
    10. Ribeiro, Ricardo, 2010. "Consumer demand for variety: intertemporal effects of consumption, product switching and pricing policies," MPRA Paper 25812, University Library of Munich, Germany.
    11. Igal Hendel & Aviv Nevo, 2006. "Measuring the Implications of Sales and Consumer Inventory Behavior," Econometrica, Econometric Society, vol. 74(6), pages 1637-1673, November.
    12. Thomas H. Jørgensen & Maxime Tô, 2020. "Robust Estimation of Finite Horizon Dynamic Economic Models," Computational Economics, Springer;Society for Computational Economics, vol. 55(2), pages 499-509, February.
    13. Harikesh Nair, 2007. "Intertemporal price discrimination with forward-looking consumers: Application to the US market for console video-games," Quantitative Marketing and Economics (QME), Springer, vol. 5(3), pages 239-292, September.
    14. Stephen P. Ryan & Mar Reguant & Meredith Fowlie, 2011. "Pollution Permits and the Evolution of Market Structure," 2011 Meeting Papers 1440, Society for Economic Dynamics.
    15. Jean-Pierre Dubé & Günter Hitsch & Puneet Manchanda, 2005. "An Empirical Model of Advertising Dynamics," Quantitative Marketing and Economics (QME), Springer, vol. 3(2), pages 107-144, June.
    16. Hugo Benitez-Silva, 2000. "A Dynamic Model of Labor Supply, Consumption/Saving, and Annuity Decisions under Uncertainty," Department of Economics Working Papers 00-06, Stony Brook University, Department of Economics.
    17. Gamba, Andrea & Tesser, Matteo, 2009. "Structural estimation of real options models," Journal of Economic Dynamics and Control, Elsevier, vol. 33(4), pages 798-816, April.
    18. Karun Adusumilli & Friedrich Geiecke & Claudio Schilter, 2019. "Dynamically Optimal Treatment Allocation," Papers 1904.01047, arXiv.org, revised Nov 2024.
    19. Chan, Tat Y. & Narasimhan, Chakravarthi & Yoon, Yeujun, 2017. "Advertising and price competition in a manufacturer-retailer channel," International Journal of Research in Marketing, Elsevier, vol. 34(3), pages 694-716.
    20. Hugo Benitez-Silva, 2001. "A Dynamic Model of Job Search Behavior over the Life Cycle with Empirical Applications," Computing in Economics and Finance 2001 100, Society for Computational Economics.
    21. Hugo Benítez-Silva, 2003. "The Annuity Puzzle Revisited," Working Papers wp055, University of Michigan, Michigan Retirement Research Center.

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