2016/16 | LEM Working Paper Series | ||||||||||||||||
A Method for Agent-Based Models Validation |
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Mattia Guerini and Alessio Moneta |
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Keywords | |||||||||||||||||
Models validation; Agent-Based models; Causality; Structural Vector Autoregressions.
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JEL Classifications | |||||||||||||||||
C32, C52, E37
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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.
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