A Method for Agent-Based Models Validation

Mattia Guerini and Alessio Moneta

LEM Papers Series from Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy

Abstract: This paper proposes a new method for empirically validate simulation models that generate artificial time series data comparable with real-world data. The approach is based on comparing structures of vector autoregression models which are estimated from both artificial and real-world data by means of causal search algorithms. This relatively simple procedure is able to tackle both the problem of confronting theoretical simulation models with the data and the problem of comparing different models in terms of their empirical reliability. Moreover the paper provides an application of the validation procedure to the Dosi et al. (2015) macro-model.

Keywords: Models validation; Agent-Based models; Causality; Structural Vector Autoregressions (search for similar items in EconPapers)
Date: 2016-12-04
New Economics Papers: this item is included in nep-cmp, nep-ecm and nep-hme
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Citations: View citations in EconPapers (47)

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Related works:
Journal Article: A method for agent-based models validation (2017)
Working Paper: A Method for Agent-Based Models Validation (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:ssa:lemwps:2016/16

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