Calibrating dynamic factor models with genetic algorithms

Abstract

In this work, we address the problem of calibrating dynamic factor models for macroeconomic forecasting. The variables upon which the forecasts are computed are the logarithm of the Industrial Production (IP) and the yearly change of the logarithm of the Consumer Price Index (CPI). Our purpose is to provide a contribution to the model identification by proposing a new kind of calibration of static and dynamic factor models. The innovative part of our work consists of building a genetic algorithm for calibrating three dynamic factor models. We first analyse a dataset of 176 EU macroeconomic and financial time series and then we conduct the same study on a dataset of 115 US macroeconomic and financial time series. In both studies, the employment of genetic algorithm in the calibration procedure produces very good results and more significant than those achieved in similar studies, such as [1, 2].