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

Kleyton da Costa, Felipe Leite Coelho da Silva, Josiane da Silva Cordeiro Coelho and Andr\'e de Melo Modenesi

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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.

Date: 2020-10, Revised 2022-03
New Economics Papers: this item is included in nep-ets and nep-for
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