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Should Macroeconomic Policy Makers Consider Parameter Covariances?

Author

Listed:
  • Hans M. Amman

    (University of Amsterdam)

  • David Kendrick

    (University of Texas)

Abstract

Many macroeconomic policy exercises consider the mean values of parameter estimates but do not use the variances and covariances. One can argue that the uncertainty of these parameter estimates is sufficiently small that it can safely be ignored. Or one can take the position that this kind of uncertainty cannot be avoided no matter what one does. Thus it is just as well to ignore it while making policy decisions. In this paper we address both of these positions in the presence of learning and find that they are unconvincing. To the contrary, we find evidence that the potential damage from ignoring the variances and covariances of the parameter estimates is substantial and that taking them into account can improve matters. Citation Copyright 1999 by Kluwer Academic Publishers.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Hans M. Amman & David Kendrick, "undated". "Should Macroeconomic Policy Makers Consider Parameter Covariances?," Computing in Economics and Finance 1997 8, Society for Computational Economics.
    • Handle: RePEc:sce:scecf7:8
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    Cited by:

    1. P. Ruben Mercado, 2004. "The Timing of Uncertainty and the Intensity of Policy," Computational Economics, Springer;Society for Computational Economics, vol. 23(4), pages 303-313, June.
    2. 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.
    3. Fidel Gonzalez, 2008. "Optimal Policy Response with Control Parameter and Intercept Covariance," Computational Economics, Springer;Society for Computational Economics, vol. 31(1), pages 1-20, February.
    4. Halkos, George & Tsilika, Kyriaki, 2016. "Climate change impacts: Understanding the synergetic interactions using graph computing," MPRA Paper 75037, University Library of Munich, Germany.
    5. Mercado, P. Ruben & Kendrick, David A., 2000. "Caution in macroeconomic policy: uncertainty and the relative intensity of policy," Economics Letters, Elsevier, vol. 68(1), pages 37-41, July.
    6. George E. Halkos & Kyriaki D. Tsilika, 2021. "Towards Better Computational Tools for Effective Environmental Policy Planning," Computational Economics, Springer;Society for Computational Economics, vol. 58(3), pages 555-572, October.
    7. David Kendrick & Hans Amman, 2006. "A Classification System for Economic Stochastic Control Models," Computational Economics, Springer;Society for Computational Economics, vol. 27(4), pages 453-481, June.
    8. repec:use:tkiwps:2020 is not listed on IDEAS
    9. Blueschke-Nikolaeva, V. & Blueschke, D. & Neck, R., 2012. "Optimal control of nonlinear dynamic econometric models: An algorithm and an application," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3230-3240.
    10. 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.
    11. Mercado, Ruben & Kendrick, David, 1998. "Hall and Taylor´s and John Taylor´s Model in DUALI," MPRA Paper 111974, University Library of Munich, Germany.
    12. P. Mercado & David Kendrick, 2006. "Parameter Uncertainty and Policy Intensity: Some Extensions and Suggestions for Further Work," Computational Economics, Springer;Society for Computational Economics, vol. 27(4), pages 483-496, June.
    13. Reinhard Neck & Sohbet Karbuz, 2017. "Dynamic Optimization under Uncertainty: A Case Study for Austrian Macroeconomic Policies," Proceedings of International Academic Conferences 5808250, International Institute of Social and Economic Sciences.
    14. Kendrick, David A., 2005. "Stochastic control for economic models: past, present and the paths ahead," Journal of Economic Dynamics and Control, Elsevier, vol. 29(1-2), pages 3-30, January.
    15. Dag Kolsrud, 2008. "Stochastic Ceteris Paribus Simulations," Computational Economics, Springer;Society for Computational Economics, vol. 31(1), pages 21-43, February.
    16. D.A. Kendrick & H.M. Amman, 2011. "A Taylor Rule for Fiscal Policy," Working Papers 11-17, Utrecht School of Economics.
    17. Pedro Francisco Páez, 2005. "Are the Washington Consensus Policies Sustainable? Game Theoretical Assessment for the Case of Ecuador," Working Paper Series, Department of Economics, University of Utah 2005_07, University of Utah, Department of Economics.
    18. George Halkos & Georgia Argyropoulou, 2021. "Pollution and Health Effects: A Nonparametric Approach," Computational Economics, Springer;Society for Computational Economics, vol. 58(3), pages 691-714, October.
    19. Reinhard Neck & Gottfried Haber & Klaus Weyerstrass, 2010. "Optimal Deterministic and Stochastic Macroeconomic Policies for Slovenia: An Application of the OPTCON Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 36(1), pages 37-45, June.

    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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