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Simulated Maximum Likelihood Estimation of Agent-Based Models in Economics and Finance

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Network Theory and Agent-Based Modeling in Economics and Finance

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Abstract

This chapter presents a general simulation-based framework for estimation of agent-based models in economics and finance based on kernel methods. After discussing the distinguishing features between empirical estimation and calibration of economic models, the simulated maximum likelihood estimator is validated for utilization in agent-based econometrics. As the main advantage, the method allows for estimation of nonlinear models for which the analytical representation of the objective function does not exist. We test the properties and performance of the estimator in combination with the seminal Brock and Hommes (J Econ Dyn Control 22:1235–1274, 1998) asset pricing model, where the dynamics are governed by switching of agents between trading strategies based on the discrete choice approach. We also provide links to how the estimation method can be extended to multivariate macroeconomic optimization problems. Using simulation analysis, we show that the estimator consistently recovers the pseudo-true parameters with high estimation precision. We further study the impact of agents’ memory on the estimation performance and show that while memory generally deteriorates the precision, the main properties of the estimator remain unaffected.

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Notes

  1. 1.

    An acronym for “Keep it Short and Simple”, “Keep it Simple and Straightforward”, or an impolite original meaning by the U.S. Navy “Keep It Simple, Stupid”.

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Acknowledgements

Jiri Kukacka gratefully acknowledges financial support from the Czech Science Foundation under the 17-12386Y “Multifractality analysis in finance” project, from the Charles University UNCE project (UNCE/HUM/035), and from the Charles University PRIMUS program (project PRIMUS/19/HUM/17).

Author information

Authors and Affiliations

  1. Faculty of Social Sciences, Institute of Economic Studies, Charles University, Opletalova 26, 110 00, Prague 1, Czechia

    Jiri Kukacka

  2. Institute of Information Theory and Automation of the Czech Academy of Sciences, Pod Vodarenskou vezi 4, 182 00, Prague 8, Czechia

    Jiri Kukacka

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  1. Jiri Kukacka
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Correspondence to Jiri Kukacka .

Editor information

Editors and Affiliations

  1. Indian Institute of Management Ahmedabad, Ahmedabad, Gujarat, India

    Anindya S. Chakrabarti

  2. College of Liberal Arts, International Christian University, Tokyo, Japan

    Lukáš Pichl

  3. Division of Social Sciences, International Christian University, Mitaka, Tokyo, Japan

    Taisei Kaizoji

8 Appendix

8 Appendix

Table 3 Complete results for \(\beta \) estimation
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Kukacka, J. (2019). Simulated Maximum Likelihood Estimation of Agent-Based Models in Economics and Finance. In: Chakrabarti, A., Pichl, L., Kaizoji, T. (eds) Network Theory and Agent-Based Modeling in Economics and Finance. Springer, Singapore. https://doi.org/10.1007/978-981-13-8319-9_10

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  • DOI: https://doi.org/10.1007/978-981-13-8319-9_10

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  • Print ISBN: 978-981-13-8318-2

  • Online ISBN: 978-981-13-8319-9

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