[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118333-en":3,"doc-seo-118333-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118333,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Comparative Analysis of GDP Forecasting using Ensemble Tree Regression Models - Machine Learning vs. Econometric Models - Master Thesis","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.","Master Degree Program in  \nData Science and Advanced Analytics  \nMDSAA  \nComparative Analysis of GDP Forecasting using Ensemble Tree  \nRegression Models:  \nMachine Learning vs. Econometric Models  \nRicardo Paulo Barbosa de Carvalho Almeida Coelho  \nMaster Thesis  \npresented as partial requirement for obtaining a Master’s Degree in Data Science and Advanced Analytics  \nNOVA Information Management School Instituto Superior de Estatística e Gestão de Informação  \nUniversidade Nova de Lisboa  \nNOVA Information Management School Instituto Superior de Estatística e Gestão de Informação  \nUniversidade Nova de Lisboa  \nComparative Analysis of GDP Forecasting using Ensemble Tree Regression Models:  \nMachine Learning vs. Econometric Models  \nby  \nRicardo Paulo Barbosa de Carvalho Almeida Coelho  \nMaster Thesis presented as partial requirement for obtaining the Master’s degree in Data Science and Advanced Analytics, with a specialization in Business Analytics.  \nSupervised by  \nProf. Jorge Miguel Ventura Bravo, PhD  \nNOVA Information Management School &  \nUniversité Paris-Dauphine PSL  \nSTATEMENT OF INTEGRITY  \nI 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.  \nLisbon, 07/07/2024  \nABSTRACT  \nThis 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.  \nKEYWORDS  \nMachine learning; Forecasting; GDP; Portugal; Gradient Boosting Regressor  \nSustainable Development Goals (SDG):  \nTABLE OF CONTENTS  \n1. Introduction ............................................................................................................. 1  \n2. Literature review ..................................................................................................... 3  \n3. Methodology ........................................................................................................... 6  \n3.1. Methodology of the thesis ................................................................................ 6  \n3.2. OECD’s Methodology ........................................................................................ 7  \n4. Data ......................................................................................................................... 8  \n5. Machine learning techniques and model................................................................ 11  \n5.1. Outliers ........................................................................................................... 11  \n5.2. Feature selection ............................................................................................ 12  \n5.3.","cbCaig7kvUOrAvek","https://ap.wps.com/l/cbCaig7kvUOrAvek","pdf",1704062,1,54,"English","en",105,"# Introduction\n# Literature review\n# Methodology\n## Methodology of the thesis\n## OECD’s Methodology\n# Data\n# Machine learning techniques and model\n## Outliers\n## Feature selection\n## Gradient Boosting Regressor\n## Rolling window technique\n## Multiple steps ahead approach\n# Results\n# Conclusions\n# Bibliographical References\n# Appendix A\n# Appendix B\n# Appendix C\n# Appendix D\n# Appendix E\n# Appendix F\n# Appendix G\n# Appendix H\n# List of Figures","[{\"question\":\"What is the main goal of the thesis?\",\"answer\":\"To assess whether machine learning using ensemble tree regression models can forecast Portugal’s GDP growth more accurately than well-established OECD forecasts.\"},{\"question\":\"Which machine learning model performed best across horizons?\",\"answer\":\"The gradient boosting regressor consistently provided the best forecasts across all horizons tested.\"},{\"question\":\"Does machine learning fully replace traditional econometric methods in this study?\",\"answer\":\"No. The results indicate machine learning is more effective as a complementary tool for time series forecasting rather than a complete replacement.\"}]","Comparative Analysis of GDP Forecasting using Ensemble Tree Regression Models - Machine Learning vs. Econometric Models - Master Thesis | PDF",1785683121,136,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"comparative-analysis-of-gdp-forecasting-using-ensemble-tree-regression-models-machine-learning-vs-econometric-models-master-thesis","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/comparative-analysis-of-gdp-forecasting-using-ensemble-tree-regression-models-machine-learning-vs-econometric-models-master-thesis/118333/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of the thesis?","Question",{"text":75,"@type":76},"To assess whether machine learning using ensemble tree regression models can forecast Portugal’s GDP growth more accurately than well-established OECD forecasts.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning model performed best across horizons?",{"text":80,"@type":76},"The gradient boosting regressor consistently provided the best forecasts across all horizons tested.",{"name":82,"@type":73,"acceptedAnswer":83},"Does machine learning fully replace traditional econometric methods in this study?",{"text":84,"@type":76},"No. The results indicate machine learning is more effective as a complementary tool for time series forecasting rather than a complete replacement.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]