Simulated maximum likelihood estimation of agent-based models in economics and finance

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F19%3A00510031" target="_blank" >RIV/67985556:_____/19:00510031 - isvavai.cz</a>
Alternative codes found
RIV/00216208:11230/19:10407391
Result on the web
<a href="http://dx.doi.org/10.1007/978-981-13-8319-9_10" target="_blank" >http://dx.doi.org/10.1007/978-981-13-8319-9_10</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-981-13-8319-9_10" target="_blank" >10.1007/978-981-13-8319-9_10</a>

Result language
angličtina
Original language name
Simulated maximum likelihood estimation of agent-based models in economics and finance
Original language description
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.
Czech name
—
Czech description
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Project
<a href="/en/project/GJ17-12386Y" target="_blank" >GJ17-12386Y: Multifractal analysis in finance: Extreme events, portfolio and risk management, and market complexity</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Book/collection name
Network Theory and Agent-Based Modeling in Economics and Finance
ISBN
978-981-13-8318-2
Number of pages of the result
24
Pages from-to
203-226
Number of pages of the book
458
Publisher name
Springer
Place of publication
Singapore
UT code for WoS chapter
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