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

Domenico Delli Gatti, Jakob Grazzini

Risultato della ricerca: Contributo in rivistaArticolo in rivista

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.
Lingua originaleEnglish
pagine (da-a)875-902
Numero di pagine28
RivistaJOURNAL OF ECONOMIC BEHAVIOR & ORGANIZATION
Volume178
DOI
Stato di pubblicazionePubblicato - 2020

Keywords

  • Agent Based Models
  • Estimation
  • Forecasting

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