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Network Theory and Agent-Based Modeling in Economics and Finance
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SYSNO ASEP 0510031 Document Type M - Monograph Chapter R&D Document Type Monograph Chapter Title Simulated maximum likelihood estimation of agent-based models in economics and finance Author(s) Kukačka, Jiří (UTIA-B) RID, ORCID Number of authors 1 Source Title Network Theory and Agent-Based Modeling in Economics and Finance. - Singapore : Springer, 2019 / Chakrabarti A. S. ; Pichl L. ; Kaizoji T. - ISBN 978-981-13-8318-2 Pages s. 203-226 Number of pages 24 s. Number of pages 458 Publication form Print - P Language eng - English Country SG - Singapore Keywords simulation-based framework ; kernel methods ; economic models Subject RIV AH - Economics OECD category Finance R&D Projects GJ17-12386Y GA ČR - Czech Science Foundation (CSF) Institutional support UTIA-B - RVO:67985556 DOI 10.1007/978-981-13-8319-9_10 Annotation 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. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2020
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