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A Method for Agent-Based Models Validation

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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.