Rising to the challenge : Bayesian estimation and forecasting techniques for macroeconomic agent-based models

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 oneperiod ahead output-gap and investment with a remarkable degree of accuracy.