IDEAS home Printed from https://ideas.repec.org/p/cdl/agrebk/qt5d98g7wg.html
   My bibliography  Save this paper

The Information Theoretic Foundations of a Probabilistic and Predictive Micro and Macro Economics

Author

Listed:
  • Judge, George

Abstract

Despite the productive efforts of economists, the disequilibrium nature of the economic system and imprecise predictions persist. One reason for this outcome is that traditional econometric models and estimation and inference methods cannot provide the necessary quantitative information for the causal influence-dynamic micro and macro questions we need to ask given the noisy indirect effects data we use. To move economics in the direction of a probabilistic and causal based predictive science, in this paper information theoretic estimation and inference methods are suggested as a basis for understanding and making predictions about dynamic micro and macro economic processes and systems.

Suggested Citation

  • Judge, George, 2012. "The Information Theoretic Foundations of a Probabilistic and Predictive Micro and Macro Economics," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt5d98g7wg, Department of Agricultural & Resource Economics, UC Berkeley.
  • Handle: RePEc:cdl:agrebk:qt5d98g7wg
    as

    Download full text from publisher

    File URL: https://www.escholarship.org/uc/item/5d98g7wg.pdf;origin=repeccitec
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Joseph E. Stiglitz, 2011. "Rethinking Macroeconomics: What Failed, And How To Repair It," Journal of the European Economic Association, European Economic Association, vol. 9(4), pages 591-645, August.
    2. Kristensen, Dennis & Shin, Yongseok, 2012. "Estimation of dynamic models with nonparametric simulated maximum likelihood," Journal of Econometrics, Elsevier, vol. 167(1), pages 76-94.
    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. Zhang, Jinyu & Zhang, Qiaosen & Li, Yong & Wang, Qianchao, 2023. "Sequential Bayesian inference for agent-based models with application to the Chinese business cycle," Economic Modelling, Elsevier, vol. 126(C).
    2. Grazzini, Jakob & Richiardi, Matteo, 2015. "Estimation of ergodic agent-based models by simulated minimum distance," Journal of Economic Dynamics and Control, Elsevier, vol. 51(C), pages 148-165.
    3. Jakob Grazzini & Matteo G. Richiardi, 2013. "Consistent Estimation of Agent-Based Models by Simulated Minimum Distance," LABORatorio R. Revelli Working Papers Series 130, LABORatorio R. Revelli, Centre for Employment Studies.
    4. Born, Benjamin & Enders, Zeno & Müller, Gernot J., 2023. "On FIRE, news, and expectations," Working Papers 42, German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin.
    5. Wang, Yi-Ran & Ma, Chao-Qun & Ren, Yi-Shuai, 2022. "A model for CBDC audits based on blockchain technology: Learning from the DCEP," Research in International Business and Finance, Elsevier, vol. 63(C).
    6. Benchimol, Jonathan & Bounader, Lahcen, 2023. "Optimal monetary policy under bounded rationality," Journal of Financial Stability, Elsevier, vol. 67(C).
    7. Giesecke, K. & Schwenkler, G., 2019. "Simulated likelihood estimators for discretely observed jump–diffusions," Journal of Econometrics, Elsevier, vol. 213(2), pages 297-320.
    8. Kristensen, Dennis & Mogensen, Patrick K. & Moon, Jong Myun & Schjerning, Bertel, 2021. "Solving dynamic discrete choice models using smoothing and sieve methods," Journal of Econometrics, Elsevier, vol. 223(2), pages 328-360.
    9. Guerini, Mattia & Napoletano, Mauro & Roventini, Andrea, 2018. "No man is an Island: The impact of heterogeneity and local interactions on macroeconomic dynamics," Economic Modelling, Elsevier, vol. 68(C), pages 82-95.
    10. Tamotsu Onozaki, 2018. "Nonlinearity, Bounded Rationality, and Heterogeneity," Springer Books, Springer, number 978-4-431-54971-0, March.
    11. Tamas Dusek, 2013. "Two modes of spatial economy models: Thünen and Krugman," ERSA conference papers ersa13p828, European Regional Science Association.
    12. Francesco Saraceno & Roberto Tamborini, 2015. "How can it work? On the impact of quantitative easing in the Eurozone," DEM Working Papers 2015/03, Department of Economics and Management.
    13. Bruins, Marianne & Duffy, James A. & Keane, Michael P. & Smith, Anthony A., 2018. "Generalized indirect inference for discrete choice models," Journal of Econometrics, Elsevier, vol. 205(1), pages 177-203.
    14. Giovanni Dosi & Mauro Napoletano & Andrea Roventini & Tania Treibich, 2016. "The Short- and Long-Run Damages of Fiscal Austerity: Keynes beyond Schumpeter," International Economic Association Series, in: Joseph E. Stiglitz & Martin Guzman (ed.), Contemporary Issues in Macroeconomics, chapter 8, pages 79-100, Palgrave Macmillan.
    15. Lilit Popoyan, 2020. "Macroprudential Policy: a Blessing or a Curse?," Review of Economics and Institutions, Università di Perugia, vol. 11(1-2).
    16. repec:spo:wpmain:info:hdl:2441/53r60a8s3kup1vc9l564k4686 is not listed on IDEAS
    17. Dosi, Giovanni & Fagiolo, Giorgio & Napoletano, Mauro & Roventini, Andrea, 2013. "Income distribution, credit and fiscal policies in an agent-based Keynesian model," Journal of Economic Dynamics and Control, Elsevier, vol. 37(8), pages 1598-1625.
    18. Luca Riccetti & Alberto Russo & Mauro Gallegati, 2022. "Firm–bank credit network, business cycle and macroprudential policy," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 17(2), pages 475-499, April.
    19. Giovanni Dosi & Mauro Napoletano & Andrea Roventini & Joseph E. Stiglitz & Tania Treibich, 2020. "Rational Heuristics? Expectations And Behaviors In Evolving Economies With Heterogeneous Interacting Agents," Economic Inquiry, Western Economic Association International, vol. 58(3), pages 1487-1516, July.
    20. Kristensen, Dennis, 2009. "Uniform Convergence Rates Of Kernel Estimators With Heterogeneous Dependent Data," Econometric Theory, Cambridge University Press, vol. 25(5), pages 1433-1445, October.
    21. repec:osf:osfxxx:2fjev_v1 is not listed on IDEAS
    22. Giorgio Fagiolo & Andrea Roventini, 2017. "Macroeconomic Policy in DSGE and Agent-Based Models Redux: New Developments and Challenges Ahead," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 20(1), pages 1-1.

    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:cdl:agrebk:qt5d98g7wg. 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: Lisa Schiff (email available below). General contact details of provider: https://edirc.repec.org/data/dabrkus.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.