Rising to the Challenge: Bayesian Estimation and Forecasting Techniques for Macroeconomic Agent-Based Models
Domenico Delli Gatti () and
No 7894, CESifo Working Paper Series from CESifo Group Munich
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.
Keywords: agent-based models; estimation; forecasting (search for similar items in EconPapers)
JEL-codes: C11 C13 C53 C63 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cmp, nep-ecm, nep-for, nep-hme, nep-mac and nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:ces:ceswps:_7894
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