A Systematic Comparison of Forecasting for Gross Domestic Product in an Emergent Economy

Abstract: Gross domestic product (GDP) is an important economic indicator that aggregates useful information to assist economic agents and policymakers in their decision-making process. In this context, GDP forecasting becomes a powerful decision optimization tool in several areas. In order to contribute in this direction, we investigated the efficiency of classical time series models, the state-space models, and the neural network models, applied to Brazilian gross domestic product. The models used were: a Seasonal Autoregressive Integrated Moving Average (SARIMA) and a Holt-Winters method, which are classical time series models; the dynamic linear model, a state-space model; and neural network autoregression and the multilayer perceptron, artificial neural network models. Based on statistical metrics of model comparison, the multilayer perceptron presented the best in-sample and out-sample forecasting performance for the analyzed period, also incorporating the growth rate structure significantly.