Forecasting Brazilian macroeconomics variables using high-dimensional time series

Abstract

This paper analyzes the performance of high-dimensional factor models to forecast four Brazilian
macroeconomic variables: two real variables, unemployment rate and industrial production
index, and two nominal variables, IPCA and IPC. The factors are estimated from a data set
containing 117 macroeconomic variables. We applied techniques to improve factor models forecasts. Methods of statistical learning are applied aims to increase the performance of factors
models. Three types of statistical learning techniques are used: shrinkage methods, forecast
combinations, and selection of preditors. The factors are extracted using supervised and unsupervised
version. The results indicate that statistical learning improves forecasts performance.
The combination of statistical learning and supervised factor models is more accurate than all
other models, with exception to the industrial production index which is best forecasted by
unsupervised factor model without statistical learning.