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Erscheinungsjahr: 
2019
Schriftenreihe/Nr.: 
CESifo Working Paper No. 7894
Verlag: 
Center for Economic Studies and ifo Institute (CESifo), Munich
Zusammenfassung: 
We propose two novel methods to “bring ABMs to the data”. First, we put forward a new Bayesian procedure to estimate the numerical values of ABM parameters that takes into account the time structure of simulated and observed time series. Second, we propose a method to forecast aggregate time series using data obtained from the simulation of an ABM. We apply our methodological contributions to a medium-scale macro agent-based model. We show that the estimated model is capable of reproducing features of observed data and of forecasting one-period ahead output-gap and investment with a remarkable degree of accuracy.
Schlagwörter: 
agent-based models
estimation
forecasting
JEL: 
C11
C13
C53
C63
Dokumentart: 
Working Paper
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