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

TL;DR

This paper systematically compares classical, state-space, and neural network models for forecasting Brazil's GDP, finding neural networks, especially multilayer perceptrons, outperform others in accuracy.

Contribution

It provides a comprehensive evaluation of different forecasting models applied to a developing economy, highlighting neural networks' superior performance.

Findings

Multilayer perceptron achieved the best forecasting accuracy.

Neural network models significantly incorporated growth rate structures.

Classical and state-space models were less accurate in this context.

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.