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A method for agent-based models validation

Mattia Guerini and Alessio Moneta ()

Journal of Economic Dynamics and Control, 2017, vol. 82, issue C, 125-141

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 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 agent-based macroeconomic model proposed by Dosi et al. (2015).

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)
Date: 2017
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Working Paper: A Method for Agent-Based Models Validation (2016) Downloads
Working Paper: A Method for Agent-Based Models Validation Downloads
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