IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/11384.html
   My bibliography  Save this paper

Grid-enabled estimation of structural economic models

Author

Listed:
  • Zhorin, Victor
  • Stef-Praun, Tiberiu

Abstract

In this paper we present our experiences with the execution of structural economic models over the Grid using ”cloud computing”. We describe cases of distributed implementation and execution of occupational choice and financial deepening models of economic growth. We show how the application of Grid technology and resources naturally fits the studies of economic systems, by allowing us to capture effects of computationally challenging real-world characteristics such as heterogeneity of wealth, talent and access costs among economic agents.

Suggested Citation

  • Zhorin, Victor & Stef-Praun, Tiberiu, 2008. "Grid-enabled estimation of structural economic models," MPRA Paper 11384, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:11384
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/11384/1/MPRA_paper_11384.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gine, Xavier & Townsend, Robert M., 2004. "Evaluation of financial liberalization: a general equilibrium model with constrained occupation choice," Journal of Development Economics, Elsevier, vol. 74(2), pages 269-307, August.
    2. Tesfatsion, Leigh & Judd, Kenneth L., 2006. "Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics," Staff General Research Papers Archive 10368, Iowa State University, Department of Economics.
    3. Tesfatsion, Leigh, 2006. "Agent-Based Computational Economics: A Constructive Approach to Economic Theory," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 16, pages 831-880, Elsevier.
    4. Jeong, Hyeok & Townsend, Robert M., 2008. "Growth And Inequality: Model Evaluation Based On An Estimation-Calibration Strategy," Macroeconomic Dynamics, Cambridge University Press, vol. 12(S2), pages 231-284, September.
    5. William Easterly, 2002. "The Elusive Quest for Growth: Economists' Adventures and Misadventures in the Tropics," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262550423, December.
    6. Leigh Tesfatsion & Kenneth L. Judd (ed.), 2006. "Handbook of Computational Economics," Handbook of Computational Economics, Elsevier, edition 1, volume 2, number 2.
    7. Banerjee, Abhijit V. & Duflo, Esther, 2005. "Growth Theory through the Lens of Development Economics," Handbook of Economic Growth, in: Philippe Aghion & Steven Durlauf (ed.), Handbook of Economic Growth, edition 1, volume 1, chapter 7, pages 473-552, Elsevier.
    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. Klaus Jaffe, 2015. "Agent based simulations visualize Adam Smith's invisible hand by solving Friedrich Hayek's Economic Calculus," Papers 1509.04264, arXiv.org, revised Nov 2015.
    2. Lovric, M. & Kaymak, U. & Spronk, J., 2008. "A Conceptual Model of Investor Behavior," ERIM Report Series Research in Management ERS-2008-030-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    3. Francesco Lamperti & Giovanni Dosi & Mauro Napoletano & Andrea Roventini & Alessandro Sapio, 2018. "And then he wasn't a she : Climate change and green transitions in an agent-based integrated assessment model," Working Papers hal-03443464, HAL.
    4. Zhang, Hui & Cao, Libin & Zhang, Bing, 2017. "Emissions trading and technology adoption: An adaptive agent-based analysis of thermal power plants in China," Resources, Conservation & Recycling, Elsevier, vol. 121(C), pages 23-32.
    5. Ashraf, Quamrul & Gershman, Boris & Howitt, Peter, 2017. "Banks, market organization, and macroeconomic performance: An agent-based computational analysis," Journal of Economic Behavior & Organization, Elsevier, vol. 135(C), pages 143-180.
    6. Richard Holt & J. Barkley Rosser & David Colander, 2011. "The Complexity Era in Economics," Review of Political Economy, Taylor & Francis Journals, vol. 23(3), pages 357-369.
    7. Fenintsoa Andriamasinoro & Raphael Danino-Perraud, 2021. "Use of artificial intelligence to assess mineral substance criticality in the French market: the example of cobalt," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 34(1), pages 19-37, April.
    8. Sensfuß, Frank & Ragwitz, Mario & Genoese, Massimo & Möst, Dominik, 2007. "Agent-based simulation of electricity markets: a literature review," Working Papers "Sustainability and Innovation" S5/2007, Fraunhofer Institute for Systems and Innovation Research (ISI).
    9. Доможиров Д. А. & Ибрагимов Н. М. & Мельникова Л. В. & Цыплаков А. А., 2017. "Интеграция подхода «затраты – выпуск» в агент-ориентированное моделирование. Часть 1. Методологические основы. Integration of input–output approach into agent-based modeling. Part 1. Methodological pr," Мир экономики и управления // Вестник НГУ. Cерия: Cоциально-экономические науки, Socionet;Новосибирский государственный университет, vol. 17(1), pages 86-99.
    10. repec:zbw:iamodp:109915 is not listed on IDEAS
    11. Giorgio Fagiolo & Mattia Guerini & Francesco Lamperti & Alessio Moneta & Andrea Roventini, 2017. "Validation of Agent-Based Models in Economics and Finance," LEM Papers Series 2017/23, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    12. Gräbner, Claudius, 2016. "From realism to instrumentalism - and back? Methodological implications of changes in the epistemology of economics," MPRA Paper 71933, University Library of Munich, Germany.
    13. Polyzos, Stathis & Samitas, Aristeidis & Katsaiti, Marina-Selini, 2020. "Who is unhappy for Brexit? A machine-learning, agent-based study on financial instability," International Review of Financial Analysis, Elsevier, vol. 72(C).
    14. Emanuele Ciola & Edoardo Gaffeo & Mauro Gallegati, 2021. "Search for Profits and Business Fluctuations: How Banks' Behaviour Explain Cycles?," Working Papers 450, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    15. Oeffner, Marc, 2008. "Agent–Based Keynesian Macroeconomics - An Evolutionary Model Embedded in an Agent–Based Computer Simulation," MPRA Paper 18199, University Library of Munich, Germany, revised Oct 2009.
    16. Paul De Grauwe, 2012. "Booms and busts: New Keynesian and behavioural explanations," Chapters, in: Robert M. Solow & Jean-Philippe Touffut (ed.), What’s Right with Macroeconomics?, chapter 6, pages 149-180, Edward Elgar Publishing.
    17. Coronese, Matteo & Occelli, Martina & Lamperti, Francesco & Roventini, Andrea, 2023. "AgriLOVE: Agriculture, land-use and technical change in an evolutionary, agent-based model," Ecological Economics, Elsevier, vol. 208(C).
    18. Jonathan F. Cogliano & Roberto Veneziani & Naoki Yoshihara, 2022. "Computational methods and classical‐Marxian economics," Journal of Economic Surveys, Wiley Blackwell, vol. 36(2), pages 310-349, April.
    19. Jens J. Krüger, 2008. "Productivity And Structural Change: A Review Of The Literature," Journal of Economic Surveys, Wiley Blackwell, vol. 22(2), pages 330-363, April.
    20. Richters, Oliver, 2021. "Modeling the out-of-equilibrium dynamics of bounded rationality and economic constraints," Journal of Economic Behavior & Organization, Elsevier, vol. 188(C), pages 846-866.
    21. Torsten Trimborn & Philipp Otte & Simon Cramer & Maximilian Beikirch & Emma Pabich & Martin Frank, 2020. "SABCEMM: A Simulator for Agent-Based Computational Economic Market Models," Computational Economics, Springer;Society for Computational Economics, vol. 55(2), pages 707-744, February.

    More about this item

    Keywords

    occupational choice; financial deepening; economic growth; cloud computing;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D58 - Microeconomics - - General Equilibrium and Disequilibrium - - - Computable and Other Applied General Equilibrium Models

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:pra:mprapa:11384. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    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.