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.

Language
Englisch

Bibliographic citation
Series: LEM Working Paper Series ; No. 2016/16

Classification
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
Subject
Models validation
Agent-Based models
Causality
Structural Vector Autoregressions

Event
Geistige Schöpfung
(who)
Guerini, Mattia
Moneta, Alessio
Event
Veröffentlichung
(who)
Scuola Superiore Sant'Anna, Laboratory of Economics and Management (LEM)
(where)
Pisa
(when)
2016

Handle
Last update
10.03.2025, 11:42 AM CET

Data provider

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Object type

  • Arbeitspapier

Associated

  • Guerini, Mattia
  • Moneta, Alessio
  • Scuola Superiore Sant'Anna, Laboratory of Economics and Management (LEM)

Time of origin

  • 2016

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