Calibration of Agent-Based Models by Means of Meta-Modeling and Nonparametric Regression

Siyan Chen () and Saul Desiderio
Additional contact information
Siyan Chen: Business School, Shantou University

Computational Economics, 2022, vol. 60, issue 4, No 11, 1457-1478

Abstract: Abstract Taking agent-based models to the data is still very challenging for researchers. In this paper we propose a new method to calibrate the model parameters based on indirect inference, which consists in minimizing the distance between real and artificial data. Basically, we first introduce a nonparametric regression meta-model to approximate the relationship between model parameters and distance. Then the meta-model is estimated by local polynomial regression estimation on a small sample of parameter vectors drawn from the parameter space of the ABM. Finally, once the distance has been estimated we can pick the parameter vector minimizing it. One innovative feature of the method is the sampling scheme, based on sampling at the same time both the parameter vectors and the seed of the random numbers generator in a random fashion, which permits to average out the effect of randomness without resorting to Monte Carlo simulations. A battery of simple calibration exercises performed on an agent-based macro model shows that the method allows to minimize the distance with good precision using relatively few simulations of the model.

Keywords: Agent-based models; Indirect calibration; Meta-modeling; Nonparametric regression; Local polynomial estimation; C14; C15; C52; C63 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10614-021-10188-5 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:kap:compec:v:60:y:2022:i:4:d:10.1007_s10614-021-10188-5

Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2

DOI: 10.1007/s10614-021-10188-5

Access Statistics for this article

Computational Economics is currently edited by Hans Amman

More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().