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Mitigation Of The Lucas Critique With Stochastic Control Methods

Author

Listed:
  • Hans Amman

    (University of Amsterdam)

  • David Kendrick

    (University of Texas at Austin)

Abstract

Lucas (1976) pointed out, that when optimization is performed on a deterministic macro model, the resulting policy may not reflect the true optimal solution. Private agents may react to announced policies and consequently model parameters will start to drift. The aim of this paper is to develop a methodology for deriving an optimal policy in the presence of rational expectations and parameter drift. This drift is captured by a stochastic optimization framework with time varying parameters. The resulting optimal policy is capable of tracking changes in the parameters due to policy changes. A numerical example illustrates how the methodology provides a way to mitigate the effects of the Lucas critique.

Suggested Citation

  • Hans Amman & David Kendrick, 2000. "Mitigation Of The Lucas Critique With Stochastic Control Methods," Computing in Economics and Finance 2000 182, Society for Computational Economics.
    • Handle: RePEc:sce:scecf0:182
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    Cited by:

    1. is not listed on IDEAS
    2. Stephen J. Turnovsky, 2011. "Stabilization Theory and Policy: 50 Years after the Phillips Curve," Economica, London School of Economics and Political Science, vol. 78(309), pages 67-88, January.
    3. D.A. Kendrick & H.M. Amman & M.P. Tucci, 2008. "Learning About Learning in Dynamic Economic Models," Working Papers 08-20, Utrecht School of Economics.
    4. Guhl, Daniel & Baumgartner, Bernhard & Kneib, Thomas & Steiner, Winfried J., 2018. "Estimating time-varying parameters in brand choice models: A semiparametric approach," International Journal of Research in Marketing, Elsevier, vol. 35(3), pages 394-414.
    5. Halkos, George & Tsilika, Kyriaki, 2016. "Climate change impacts: Understanding the synergetic interactions using graph computing," MPRA Paper 75037, University Library of Munich, Germany.
    6. Athanasiou, George & Kotsios, Stelios, 2008. "An algorithmic approach to exchange rate stabilization," Economic Modelling, Elsevier, vol. 25(6), pages 1246-1260, November.
    7. repec:use:tkiwps:2020 is not listed on IDEAS
    8. Halkos, George E. & Tsilika, Kyriaki D., 2017. "Climate change effects and their interactions: An analysis aiming at policy implications," Economic Analysis and Policy, Elsevier, vol. 53(C), pages 140-146.
    9. Amman, Hans M. & Kendrick, David A. & Tucci, Marco P., 2020. "Approximating The Value Function For Optimal Experimentation," Macroeconomic Dynamics, Cambridge University Press, vol. 24(5), pages 1073-1086, July.
    10. David Kendrick & George Shoukry, 2014. "Quarterly Fiscal Policy Experiments with a Multiplier-Accelerator Model," Computational Economics, Springer;Society for Computational Economics, vol. 44(3), pages 269-293, October.
    11. H.M. Amman & D.A. Kendrick, 2012. "Conjectures on the policy function in the presence of optimal experimentation," Working Papers 12-09, Utrecht School of Economics.
    12. D. Blueschke & I. Savin & V. Blueschke-Nikolaeva, 2020. "An Evolutionary Approach to Passive Learning in Optimal Control Problems," Computational Economics, Springer;Society for Computational Economics, vol. 56(3), pages 659-673, October.
    13. Ilias Kostarakos & Stelios Kotsios, 2017. "Feedback policy rules for government spending: an algorithmic approach," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 6(1), pages 1-10, December.

    More about this item

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • E61 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - Policy Objectives; Policy Designs and Consistency; Policy Coordination

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