Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/174525 
Year of Publication: 
2016
Series/Report no.: 
LEM Working Paper Series No. 2016/16
Publisher: 
Scuola Superiore Sant'Anna, Laboratory of Economics and Management (LEM), Pisa
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.
Subjects: 
Models validation
Agent-Based models
Causality
Structural Vector Autoregressions
JEL: 
C32
C52
E37
Document Type: 
Working Paper

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