Arbeitspapier
A method for agent-based models validation
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.
- Sprache
-
Englisch
- Erschienen in
-
Series: LEM Working Paper Series ; No. 2016/16
- Klassifikation
-
Wirtschaft
Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
Model Evaluation, Validation, and Selection
Prices, Business Fluctuations, and Cycles: Forecasting and Simulation: Models and Applications
- Thema
-
Models validation
Agent-Based models
Causality
Structural Vector Autoregressions
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Guerini, Mattia
Moneta, Alessio
- Ereignis
-
Veröffentlichung
- (wer)
-
Scuola Superiore Sant'Anna, Laboratory of Economics and Management (LEM)
- (wo)
-
Pisa
- (wann)
-
2016
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:42 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Guerini, Mattia
- Moneta, Alessio
- Scuola Superiore Sant'Anna, Laboratory of Economics and Management (LEM)
Entstanden
- 2016
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