Rising to the Challenge: Bayesian Estimation and Forecasting Techniques for Macroeconomic Agent-Based Models

Domenico Delli Gatti and Jakob Grazzini

No 7894, CESifo Working Paper Series from CESifo

Abstract: 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)
Date: 2019
New Economics Papers: this item is included in nep-cmp, nep-ecm, nep-for, nep-hme, nep-mac and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Related works:
Journal Article: Rising to the challenge: Bayesian estimation and forecasting techniques for macroeconomic Agent Based Models (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:ces:ceswps:_7894

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