IDEAS home Printed from https://ideas.repec.org/a/ijm/journl/v9y2016i3p1-4.html
   My bibliography  Save this article

Editorial

Author

Listed:
  • Matteo Richiardi

    (Institute for New Economic Thinking, Oxford Martin School, University of Oxford, Eagle House, Walton Well Road, OX2 6ED Oxford, UK)

Abstract

No abstract is available for this item.

Suggested Citation

  • Matteo Richiardi, 2016. "Editorial," International Journal of Microsimulation, International Microsimulation Association, vol. 9(3), pages 1-4.
    • Handle: RePEc:ijm:journl:v:9:y:2016:i:3:p:1-4
    as

    Download full text from publisher

    File URL: http://www.microsimulation.org/IJM/V9_3/0_IJM_9_3_2016_Editorial.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Barde, Sylvain, 2016. "Direct comparison of agent-based models of herding in financial markets," Journal of Economic Dynamics and Control, Elsevier, vol. 73(C), pages 329-353.
    2. repec:hal:spmain:info:hdl:2441/7kr9gv74ut9ngo58gia97t83i7 is not listed on IDEAS
    3. Jinjing Li & Cathal O'Donoghue, 2014. "Evaluating Binary Alignment Methods in Microsimulation Models," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 17(1), pages 1-15.
    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. Barde, Sylvain, 2020. "Macroeconomic simulation comparison with a multivariate extension of the Markov information criterion," Journal of Economic Dynamics and Control, Elsevier, vol. 111(C).
    2. Lamperti, Francesco & Roventini, Andrea & Sani, Amir, 2018. "Agent-based model calibration using machine learning surrogates," Journal of Economic Dynamics and Control, Elsevier, vol. 90(C), pages 366-389.
    3. 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.
    4. Donovan Platt, 2022. "Bayesian Estimation of Economic Simulation Models Using Neural Networks," Computational Economics, Springer;Society for Computational Economics, vol. 59(2), pages 599-650, February.
    5. O'Donoghue, Cathal & Sologon, Denisa Maria, 2023. "The Transformation of Public Policy Analysis in Times of Crisis – A Microsimulation-Nowcasting Method Using Big Data," IZA Discussion Papers 15937, Institute of Labor Economics (IZA).
    6. Matteo Richiardi & Ross E. Richardson, 2017. "JAS-mine: A new platform for microsimulation and agent-based modelling," International Journal of Microsimulation, International Microsimulation Association, vol. 10(1), pages 106-134.
    7. repec:hal:spmain:info:hdl:2441/13thfd12aa8rmplfudlgvgahff is not listed on IDEAS
    8. Emna Mnif & Anis Jarboui & M. Kabir Hassan & Khaireddine Mouakhar, 2020. "Big data tools for Islamic financial analysis," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 27(1), pages 10-21, January.
    9. Tubbenhauer, Tobias & Fieberg, Christian & Poddig, Thorsten, 2021. "Multi-agent-based VaR forecasting," Journal of Economic Dynamics and Control, Elsevier, vol. 131(C).
    10. Sylvain Barde & Sander Van Der Hoog, 2017. "An empirical validation protocol for large-scale agent-based models," Sciences Po publications 17/12, Sciences Po.
    11. Richiardi, Matteo & Bronka, Patryk & van de Ven, Justin, 2023. "Back to the future: Agent-based modelling and dynamic microsimulation," Centre for Microsimulation and Policy Analysis Working Paper Series CEMPA8/23, Centre for Microsimulation and Policy Analysis at the Institute for Social and Economic Research.
    12. Schmitt, Noemi & Westerhoff, Frank, 2021. "Trend followers, contrarians and fundamentalists: Explaining the dynamics of financial markets," Journal of Economic Behavior & Organization, Elsevier, vol. 192(C), pages 117-136.
    13. Platt, Donovan & Gebbie, Tim, 2018. "Can agent-based models probe market microstructure?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 1092-1106.
    14. Zhenxi Chen & Thomas Lux, 2018. "Estimation of Sentiment Effects in Financial Markets: A Simulated Method of Moments Approach," Computational Economics, Springer;Society for Computational Economics, vol. 52(3), pages 711-744, October.
    15. Francesco Lamperti, 2018. "Empirical validation of simulated models through the GSL-div: an illustrative application," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 13(1), pages 143-171, April.
    16. repec:hal:spmain:info:hdl:2441/20hflp7eqn97boh50no50tv67n is not listed on IDEAS
    17. Guerini, Mattia & Moneta, Alessio, 2017. "A method for agent-based models validation," Journal of Economic Dynamics and Control, Elsevier, vol. 82(C), pages 125-141.
    18. Geyer, Johannes, 2021. "Die Folgen der Corona-Krise für die Anwartschaften an die gesetzliche Rentenversicherung," Working Paper Forschungsförderung 216, Hans-Böckler-Stiftung, Düsseldorf.
    19. Venelina Nikolova & Juan E. Trinidad Segovia & Manuel Fernández-Martínez & Miguel Angel Sánchez-Granero, 2020. "A Novel Methodology to Calculate the Probability of Volatility Clusters in Financial Series: An Application to Cryptocurrency Markets," Mathematics, MDPI, vol. 8(8), pages 1-15, July.
    20. Magalasi, Chimwemwe, 2021. "The short-term distributional impact of COVID-19 in Malawi," EUROMOD Working Papers EM7/21, EUROMOD at the Institute for Social and Economic Research.
    21. Donovan Platt & Tim Gebbie, 2016. "Can Agent-Based Models Probe Market Microstructure?," Papers 1611.08510, arXiv.org, revised Aug 2017.
    22. repec:ijm:journl:v109:y:2017:i:1:p:106-134 is not listed on IDEAS
    23. Adam Majewski & Stefano Ciliberti & Jean-Philippe Bouchaud, 2018. "Co-existence of Trend and Value in Financial Markets: Estimating an Extended Chiarella Model," Papers 1807.11751, arXiv.org.

    More about this item

    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:ijm:journl:v:9:y:2016:i:3:p:1-4. 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: Jinjing Li (email available below). General contact details of provider: http://www.microsimulation.org/ijm/ .

    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.