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    Rights statement: This is the author’s version of a work that was accepted for publication in Journal of Economic Dynamics and Control. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Economic Dynamics and Control, 77, 2017 DOI: 10.1016/j.jedc.2017.01.014

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Bayesian estimation of agent-based models

Research output: Contribution to journalJournal article

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Bayesian estimation of agent-based models. / Grazzini, Jakob; Richiardi, Matteo; Tsionas, Efthymios.

In: Journal of Economic Dynamics and Control, Vol. 77, 04.2017, p. 26-47.

Research output: Contribution to journalJournal article

Harvard

Grazzini, J, Richiardi, M & Tsionas, E 2017, 'Bayesian estimation of agent-based models', Journal of Economic Dynamics and Control, vol. 77, pp. 26-47. https://doi.org/10.1016/j.jedc.2017.01.014

APA

Grazzini, J., Richiardi, M., & Tsionas, E. (2017). Bayesian estimation of agent-based models. Journal of Economic Dynamics and Control, 77, 26-47. https://doi.org/10.1016/j.jedc.2017.01.014

Vancouver

Grazzini J, Richiardi M, Tsionas E. Bayesian estimation of agent-based models. Journal of Economic Dynamics and Control. 2017 Apr;77:26-47. https://doi.org/10.1016/j.jedc.2017.01.014

Author

Grazzini, Jakob ; Richiardi, Matteo ; Tsionas, Efthymios. / Bayesian estimation of agent-based models. In: Journal of Economic Dynamics and Control. 2017 ; Vol. 77. pp. 26-47.

Bibtex

@article{4f4bea9e612144d6bcf155f536b8ed80,
title = "Bayesian estimation of agent-based models",
abstract = "We consider Bayesian inference techniques for Agent-Based (AB) models, as an alternative to simulated minimum distance (SMD). Three computationally heavy steps are involved: (i) simulating the model, (ii) estimating the likelihood and (iii) sampling from the posterior distribution of the parameters. Computational complexity of AB models implies that efficient techniques have to be used with respect to points (ii) and (iii), possibly involving approximations. We first discuss non-parametric (kernel density) estimation of the likelihood, coupled with Markov chain Monte Carlo sampling schemes. We then turn to parametric approximations of the likelihood, which can be derived by observing the distribution of the simulation outcomes around the statistical equilibria, or by assuming a specific form for the distribution of external deviations in the data. Finally, we introduce Approximate Bayesian Computation techniques for likelihood-free estimation. These allow embedding SMD methods in a Bayesian framework, and are particularly suited when robust estimation is needed. These techniques are first tested in a simple price discovery model with one parameter, and then employed to estimate the behavioural macroeconomic model of De Grauwe (2012), with nine unknown parameters.",
keywords = "Agent-based, Estimation, Bayes, Approximate bayesian computation, Likelihood",
author = "Jakob Grazzini and Matteo Richiardi and Efthymios Tsionas",
note = "This is the author{\textquoteright}s version of a work that was accepted for publication in Journal of Economic Dynamics and Control. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Economic Dynamics and Control, 77, 2017 DOI: 10.1016/j.jedc.2017.01.014",
year = "2017",
month = apr
doi = "10.1016/j.jedc.2017.01.014",
language = "English",
volume = "77",
pages = "26--47",
journal = "Journal of Economic Dynamics and Control",
issn = "0165-1889",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Bayesian estimation of agent-based models

AU - Grazzini, Jakob

AU - Richiardi, Matteo

AU - Tsionas, Efthymios

N1 - This is the author’s version of a work that was accepted for publication in Journal of Economic Dynamics and Control. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Economic Dynamics and Control, 77, 2017 DOI: 10.1016/j.jedc.2017.01.014

PY - 2017/4

Y1 - 2017/4

N2 - We consider Bayesian inference techniques for Agent-Based (AB) models, as an alternative to simulated minimum distance (SMD). Three computationally heavy steps are involved: (i) simulating the model, (ii) estimating the likelihood and (iii) sampling from the posterior distribution of the parameters. Computational complexity of AB models implies that efficient techniques have to be used with respect to points (ii) and (iii), possibly involving approximations. We first discuss non-parametric (kernel density) estimation of the likelihood, coupled with Markov chain Monte Carlo sampling schemes. We then turn to parametric approximations of the likelihood, which can be derived by observing the distribution of the simulation outcomes around the statistical equilibria, or by assuming a specific form for the distribution of external deviations in the data. Finally, we introduce Approximate Bayesian Computation techniques for likelihood-free estimation. These allow embedding SMD methods in a Bayesian framework, and are particularly suited when robust estimation is needed. These techniques are first tested in a simple price discovery model with one parameter, and then employed to estimate the behavioural macroeconomic model of De Grauwe (2012), with nine unknown parameters.

AB - We consider Bayesian inference techniques for Agent-Based (AB) models, as an alternative to simulated minimum distance (SMD). Three computationally heavy steps are involved: (i) simulating the model, (ii) estimating the likelihood and (iii) sampling from the posterior distribution of the parameters. Computational complexity of AB models implies that efficient techniques have to be used with respect to points (ii) and (iii), possibly involving approximations. We first discuss non-parametric (kernel density) estimation of the likelihood, coupled with Markov chain Monte Carlo sampling schemes. We then turn to parametric approximations of the likelihood, which can be derived by observing the distribution of the simulation outcomes around the statistical equilibria, or by assuming a specific form for the distribution of external deviations in the data. Finally, we introduce Approximate Bayesian Computation techniques for likelihood-free estimation. These allow embedding SMD methods in a Bayesian framework, and are particularly suited when robust estimation is needed. These techniques are first tested in a simple price discovery model with one parameter, and then employed to estimate the behavioural macroeconomic model of De Grauwe (2012), with nine unknown parameters.

KW - Agent-based

KW - Estimation

KW - Bayes

KW - Approximate bayesian computation

KW - Likelihood

U2 - 10.1016/j.jedc.2017.01.014

DO - 10.1016/j.jedc.2017.01.014

M3 - Journal article

VL - 77

SP - 26

EP - 47

JO - Journal of Economic Dynamics and Control

JF - Journal of Economic Dynamics and Control

SN - 0165-1889

ER -