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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 (k...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Accepted Manuscript

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Publisher copy:
10.1016/j.jedc.2017.01.014

Authors


Grazzini, J More by this author
More by this author
Department:
Merton College
Tsionas, M More by this author
Publisher:
Elsevier Publisher's website
Journal:
Journal of Economic Dynamics and Control Journal website
Volume:
77
Pages:
26–47
Publication date:
2017-02-05
Acceptance date:
2017-01-30
DOI:
ISSN:
0165-1889
Pubs id:
pubs:680673
URN:
uri:5007ab06-3f4d-4233-bf43-3f608f22d57f
UUID:
uuid:5007ab06-3f4d-4233-bf43-3f608f22d57f
Local pid:
pubs:680673

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