Comparative Analysis of GDP Forecasting using Ensemble Tree Regression Models - Machine Learning vs. Econometric Models - Master Thesis

Aug 2, 2026

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This thesis evaluates whether machine learning can improve forecasts of Portugal’s GDP growth relative to established OECD benchmarks, using data from 1962 to 2022 collected from the OECD and the Federal Reserve’s economic datasets. Ensemble tree regression models are compared across multiple horizons, focusing on forecast accuracy and error behavior versus traditional econometric approaches. Results show machine learning generally does not outperform the OECD forecasts, though it can deliver better accuracy at specific points while incurring larger errors. Gradient boosting regressor delivers the most consistent forecasts across all horizons. The study also indicates machine learning benefits more from larger data volumes than from higher dimensionality, supporting its use as a complementary time-series tool rather than a replacement.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages54

Reading Time2 h 16 min

AccessFree to download

Introduction

Literature review

Methodology

Methodology of the thesis

OECD’s Methodology

Data

Machine learning techniques and model

Outliers

Feature selection

Gradient Boosting Regressor

Rolling window technique

Multiple steps ahead approach

Results

Conclusions

Bibliographical References

Appendix A

Appendix B

Appendix C

Appendix D

Appendix E

Appendix F

Appendix G

Appendix H

List of Figures

Master Degree Program in Data Science and Advanced Analytics MDSAA Comparative Analysis of GDP Forecasting using Ensemble Tree Regression Models: Machine Learning vs. Econometric Models Ricardo Paulo Barbosa de Carvalho Almeida Coelho Master Thesis presented as partial requirement for obtaining a Master’s Degree in Data Science and Advanced Analytics NOVA Information Management School Instituto Superior de Estatística e Gestão de Informação Universidade Nova de Lisboa NOVA Information Management School Instituto Superior de Estatística e Gestão de Informação Universidade Nova de Lisboa Comparative Analysis of GDP Forecasting using Ensemble Tree Regression Models: Machine Learning vs. Econometric Models by Ricardo Paulo Barbosa de Carvalho Almeida Coelho Master Thesis presented as partial requirement for obtaining the Master’s degree in Data Science and Advanced Analytics, with a specialization in Business Analytics. Supervised by Prof. Jorge Miguel Ventura Bravo, PhD NOVA Information Management School & Université Paris-Dauphine PSL STATEMENT OF INTEGRITY I hereby declare having conducted this academic work with integrity. I confirm that I have not used plagiarism or any form of undue use of information or falsification of results along the process leading to its elaboration. I further declare that I have fully acknowledged the Rules of Conduct and Code of Honor from the NOVA Information Management School. Lisbon, 07/07/2024 ABSTRACT 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. KEYWORDS Machine learning; Forecasting; GDP; Portugal; Gradient Boosting Regressor Sustainable Development Goals (SDG): TABLE OF CONTENTS 1. Introduction ............................................................................................................. 1 2. Literature review ..................................................................................................... 3 3. Methodology ........................................................................................................... 6 3.1. Methodology of the thesis ................................................................................ 6 3.2. OECD’s Methodology ........................................................................................ 7 4. Data ......................................................................................................................... 8 5. Machine learning techniques and model................................................................ 11 5.1. Outliers ........................................................................................................... 11 5.2. Feature selection ............................................................................................ 12 5.3.

FAQs

What is the main goal of the thesis?

To assess whether machine learning using ensemble tree regression models can forecast Portugal’s GDP growth more accurately than well-established OECD forecasts.

Which machine learning model performed best across horizons?

The gradient boosting regressor consistently provided the best forecasts across all horizons tested.

Does machine learning fully replace traditional econometric methods in this study?

No. The results indicate machine learning is more effective as a complementary tool for time series forecasting rather than a complete replacement.

Aug 2, 2026

Upload by Miles