Rising to the challenge: Bayesian estimation and forecasting techniques for macroeconomic Agent Based Models

Domenico Delli Gatti and Jakob Grazzini

Journal of Economic Behavior & Organization, 2020, vol. 178, issue C, 875-902

Abstract: We propose two novel methods to “bring Agent Based Models (ABMs) to the data”. First, we describe a 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 specific medium-scale macro ABM.

Keywords: Agent Based Models; Estimation; Forecasting (search for similar items in EconPapers)
JEL-codes: C53 C63 E17 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (23)

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Related works:
Working Paper: Rising to the Challenge: Bayesian Estimation and Forecasting Techniques for Macroeconomic Agent-Based Models (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jeborg:v:178:y:2020:i:c:p:875-902

DOI: 10.1016/j.jebo.2020.07.023

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