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The Role of Machine Learning in Macroeconomic Forecasts: A Study Focused on Forecasting GDP in Portugal
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Resumo(s)
Machine learning (ML) has been increasingly recognized as a powerful tool in
macroeconomic forecasting, with the potential to outperform traditional econometric
methods. Accurate GDP forecasting is crucial for central banks, due to its significant
influence on economic policy and financial stability. This thesis aims to evaluate the
predictive performance of ML algorithms, namely Random Forest (RF), Support Vector
Machines (SVM), and Long Short-Term Memory Neural Networks (LSTM), compared to the
traditional ARIMA model for forecasting Portugal's GDP. The study uses economic time
series data from Banco de Portugal incorporating eleven economic series covering the
period from 2011 to 2023. The models were applied to both original and differenced time
series, with differencing significantly improving performance across all algorithms. The
results showed that ML models generally outperformed ARIMA model, with RF and SVM
performing the best out of all tested models. RF was the most consistent model in
multivariate forecasts achieving the most accurate prediction, while SVM performed better
in the univariate setting, balancing robustness and forecasting precision. LSTM, on the other
hand, underperformed, likely due to dataset limitations, suggesting that deep learning
approaches may require larger volumes of data and greater model complexity to reach their
full potential. The inclusion of additional explanatory variables notably improved the ML
models’ performance, particularly for RF. The thesis demonstrates the potential of ML in
macroeconomic forecasting and points to future research and exploration opportunities in
this ongoing field.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business Analytics
Palavras-chave
GDP Portugal Forecasting Time Series Machine Learning SDG 8 - Decent work and economic growth