Detail publikace

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

Autor: PhDr. Jiří Kukačka Ph.D.,
Typ: Kapitoly v knize
Rok: 2019
Číslo: 0
ISSN / ISBN: 978-981-13-8318-2 (Print), 978-981-13-8319-9 (Online)
Publikováno v: Springer, Network Theory and Agent-Based Modeling in Economics and Finance, DOI
Místo vydání: Singapore
Klíčová slova:
JEL kódy:
Citace: Kukacka, J. (2019). Simulated maximum likelihood estimation of agent-based models in economics and finance. In: Network Theory and Agent-Based Modeling in Economics and Finance, pp. 203–226. Singapore: Springer Singapore.
Granty: PRIMUS/19/HUM/17 2019-2021 Behaviorální finance a makroekonomie: Nové pohledy pro hlavní proud
Abstrakt: 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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