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Comparative Analysis of GDP Forecasting using Ensemble Tree Regression Models: Machine Learning vs. Econometric Models
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Orientador(es)
Resumo(s)
This thesis evaluates whether machine learning can have better results than well-established
institutions in forecasting Portugal’s GDP growth using ensemble tree regression models, with
the OECD’s economic outlook forecasts for Portugal serving as a benchmark based on data
from 1962 to 2022 recovered from OECD and the Federal Reserve’s economic data. The
findings reveal that, in general, machine learning did not surpass the forecasts of the OECD.
However, machine learning demonstrated the potential for better accuracy at many points,
despite a higher propensity for larger errors compared to traditional methods. Among the
ensemble tree regression models tested, the gradient boosting regressor consistently
provided the best forecasts across all horizons, outperforming the random forest, extreme
gradient boosting, and light gradient boosting machine models. The results also suggest that
machine learning performs better with a larger volume of data than with higher
dimensionality, even if some data points seem irrelevant to forecast future values. This thesis
highlights the potential of machine learning in time series forecasting as a complementary tool
to traditional methods, rather than a complete replacement.
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
Machine learning Forecasting GDP Portugal Gradient Boosting Regressor SDG 8 - Decent work and economic growth