Rising to the Challenge: Bayesian Estimation and Forecasting Techniques for Macroeconomic Agent-Based Models
CESifo, Munich, 2019
CESifo Working Paper No. 7894
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.
Fiscal Policy, Macroeconomics and Growth
Empirical and Theoretical Methods