IDEAS home Printed from https://ideas.repec.org/a/ebl/ecbull/eb-19-00056.html

A geometric programming approach to dynamic economic models

Author

Listed:
  • Inna Tsener

    (Universitat de les Illes Balears)

Abstract

Geometric programming (GP) has several attractive features: it is tractable in large-scale problems, requires no initial guess or tuning of solver parameters, guarantees the convergence to a global optimum and can deal with kinks. In this note, I argue that GP is a potentially promising tool in economics. First, I show that a stylized finite-horizon growth model can be mapped into a GP format by using simple transformations. Second, I show that GP methods produce accurate and reliable solutions including the case of occasionally binding constraints which cannot be easily treated with conventional solvers. Examples of MATLAB codes are provided.

Suggested Citation

  • Inna Tsener, 2020. "A geometric programming approach to dynamic economic models," Economics Bulletin, AccessEcon, vol. 40(2), pages 1068-1074.
    • Handle: RePEc:ebl:ecbull:eb-19-00056
    as

    Download full text from publisher

    File URL: http://www.accessecon.com/Pubs/EB/2020/Volume40/EB-20-V40-I2-P91.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lilia Maliar & Serguei Maliar & John B. Taylor & Inna Tsener, 2020. "A tractable framework for analyzing a class of nonstationary Markov models," Quantitative Economics, Econometric Society, vol. 11(4), pages 1289-1323, November.
    2. Yongyang Cai & Kenneth Judd & Jevgenijs Steinbuks, 2017. "A nonlinear certainty equivalent approximation method for dynamic stochastic problems," Quantitative Economics, Econometric Society, vol. 8(1), pages 117-147, March.
    3. Fair, Ray C & Taylor, John B, 1983. "Solution and Maximum Likelihood Estimation of Dynamic Nonlinear Rational Expectations Models," Econometrica, Econometric Society, vol. 51(4), pages 1169-1185, July.
    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. Yongyang Cai & Kenneth L. Judd, 2023. "A simple but powerful simulated certainty equivalent approximation method for dynamic stochastic problems," Quantitative Economics, Econometric Society, vol. 14(2), pages 651-687, May.
    2. Martin Seneca, 2020. "Risk Shocks and Monetary Policy in the New Normal," International Journal of Central Banking, International Journal of Central Banking, vol. 16(6), pages 185-232, December.
    3. Fernández-Villaverde, J. & Rubio-Ramírez, J.F. & Schorfheide, F., 2016. "Solution and Estimation Methods for DSGE Models," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 527-724, Elsevier.
    4. Lilia Maliar & John B. Taylor, 2019. "Forward Guidance: Is It Useful Away from the Lower Bound?," NBER Working Papers 26053, National Bureau of Economic Research, Inc.
    5. Maliar, Lilia & Taylor, John, 2018. "Taylor Rules and Forward Guidance: A Rule is not a Path," CEPR Discussion Papers 13383, Centre for Economic Policy Research.
    6. Andrew Binning, 2022. "An Efficient Application of the Extended Path Algorithm in Matlab with Examples," Treasury Working Paper Series 22/02, New Zealand Treasury.
    7. Lilia Maliar & Serguei Maliar & John B. Taylor & Inna Tsener, 2020. "A tractable framework for analyzing a class of nonstationary Markov models," Quantitative Economics, Econometric Society, vol. 11(4), pages 1289-1323, November.
    8. Maliar, Lilia & Naubert, Christopher, 2019. "Monetary Policy Transmission with Endogenous Central Bank Responses in TANK," CEPR Discussion Papers 14159, Centre for Economic Policy Research.
    9. Maliar, Lilia & Taylor, John B., 2024. "Odyssean forward guidance in normal times," Journal of Economic Dynamics and Control, Elsevier, vol. 165(C).
    10. Adjemian, Stéphane & Juillard, Michel, 2025. "Stochastic Extended Path," Dynare Working Papers 84, CEPREMAP, revised Sep 2025.
    11. Pierre Richard Agénor & Devrim Yilmaz, 2006. "The Tyranny of Rules: Fiscal Discipline, Productive Spending, and Growth," Economics Discussion Paper Series 0616, Economics, The University of Manchester.
    12. Paul D. McNelis, 2014. "Finding Stability in a Time of Crisis: Lessons of East Asia for Eastern Europe," Working Papers 052014, Hong Kong Institute for Monetary Research.
    13. Karadi, Peter & Nakov, Anton, 2021. "Effectiveness and addictiveness of quantitative easing," Journal of Monetary Economics, Elsevier, vol. 117(C), pages 1096-1117.
    14. Burton, Diana M. & Love, H. Alan, 1996. "A Review of Alternative Expectations Regimes in Commodity Markets: Specification, Estimation, and Hypothesis Testing Using Structural Models," Agricultural and Resource Economics Review, Cambridge University Press, vol. 25(2), pages 213-231, October.
    15. S. Bogan Aruoba & Pablo Cuba-Borda & Kenji Higa-Flores & Frank Schorfheide & Sergio Villalvazo, 2021. "Piecewise-Linear Approximations and Filtering for DSGE Models with Occasionally Binding Constraints," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 41, pages 96-120, July.
    16. Mathilde Le Moigne & Francesco Saraceno & Sébastien Villemot, 2016. "Probably Too Little, Certainly Too Late. An Assessment of the Juncker Investment Plan," PSE Working Papers hal-03459360, HAL.
    17. Simon Gilchrist & John C. Williams, 2000. "Putty-Clay and Investment: A Business Cycle Analysis," Journal of Political Economy, University of Chicago Press, vol. 108(5), pages 928-960, October.
    18. Lindé, Jesper & Smets, Frank & Wouters, Rafael, 2016. "Challenges for Central Banks´ Macro Models," Working Paper Series 323, Sveriges Riksbank (Central Bank of Sweden).
    19. Sposi, Michael, 2022. "Demographics and the evolution of global imbalances," Journal of Monetary Economics, Elsevier, vol. 126(C), pages 1-14.
    20. Juillard, Michel & Laxton, Douglas & McAdam, Peter & Pioro, Hope, 1998. "An algorithm competition: First-order iterations versus Newton-based techniques," Journal of Economic Dynamics and Control, Elsevier, vol. 22(8-9), pages 1291-1318, August.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • D9 - Microeconomics - - Micro-Based Behavioral Economics

    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:ebl:ecbull:eb-19-00056. 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: John P. Conley (email available below). General contact details of provider: .

    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.