  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.
