A Method for Agent-Based Models Validation

Mattia Guerini and Alessio Moneta

No 42, Working Papers Series from Institute for New Economic Thinking

Abstract: This paper proposes a new method to 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 that 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. The paper also 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)
JEL-codes: C32 C52 E37 (search for similar items in EconPapers)
Pages: 29 pages
Date: 2016-04
New Economics Papers: this item is included in nep-cmp, nep-hme, nep-mac and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (32)

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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:thk:wpaper:42

DOI: 10.2139/ssrn.2772133

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