- 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...
Expand abstract - Publication status:
- Published
- Peer review status:
- Peer reviewed
- Version:
- Accepted Manuscript
- 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
- Copyright holder:
- Elsevier B.V.
- Copyright date:
- 2017
- Notes:
-
Copyright © 2017 Elsevier B.V. This is the accepted manuscript version of the article. The final version is available online from Elsevier at: https://doi.org/10.1016/j.jedc.2017.01.014
Journal article
Bayesian estimation of agent-based models
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