[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118988-en":3,"doc-seo-118988-105":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},118988,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Nowcasting Madagascar’s real GDP using machine learning algorithms","The study evaluates the predictive power of multiple machine learning regression models for nowcasting Madagascar’s real GDP. Models include Ridge, Lasso, Elastic-net, principal component regression, k-NN regression, linear SVR, and ensemble approaches using Random Forest and XGBoost, benchmarked against econometric methods. Using 10 quarterly macroeconomic leading indicators from 2007Q1–2022Q4, accuracy is assessed with RMSE, MAE, and MAPE. Results show ensemble aggregation consistently improves nowcast performance over traditional models, supporting timely, data-driven policy guidance.","Munich Personal RePEc Archive  \nNowcasting Madagascar’s real GDP using machine learning algorithms  \nRamaharo, Franck M. and Rasolofomanana, Gerzhino H.  \nMinistère de l’Économie et des Finances, Antananarivo 101, Madagascar  \n23 December 2023  \nOnline at [https://mpra. ub. uni-muenchen. de/119574/](https://mpra. ub. uni-muenchen. de/119574/)  \n[MPRA Paper No. 119574](MPRA Paper No. 119574) , [posted 02 Jan 2024 13:36 UTC](posted 02 Jan 2024 13:36 UTC)  \nNowcasting Madagascar’s real GDP using machine learning algorithms  \nFranck M. Ramaharo1 and Gerzhino H. Rasolofomanana2  \n1 Service de la Modélisation Économique, Ministère de l’Économie et des Finances  \n2 Service du Suivi des Indicateurs et de la Conjoncture, Ministère de l’Économie et des Finances  \nAntananarivo 101, Madagascar  \n{1 franck.ramaharo, [2](2 mrgherme}@gmail.com)[ mrgherme}@gmail.com](2 mrgherme}@gmail.com)  \nDecember 23, 2023  \nAbstract  \nWe investigate the predictive power of different machine learning algorithms to nowcast Madagascar’s gross domestic product (GDP) . We trained popular regression models, including linear regularized regression (Ridge, Lasso, Elastic-net), dimensionality reduction model (principal component regression), knearest neighbors algorithm (k-NN regression), support vector regression (linear SVR), and tree-based ensemble models (Random forest and XGBoost regressions), on 10 Malagasy quarterly macroeconomic leading indicators over the period 2007Q1–2022Q4, and we used simple econometric models as a benchmark. We measured the nowcast accuracy of each model by calculating the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) . Our findings reveal that the Ensemble Model, formed by aggregating individual predictions, consistently outperforms traditional econometric models. We conclude that machine learning models can deliver more accurate and timely nowcasts of Malagasy economic performance and provide policymakers with additional guidance for data-driven decision making.  \nKeywords: nowcasting, gross domestic product, machine learning, Madagascar  \nAvertissement. Le contenu de la présente publication n’engage que ses auteurs. Chacune des opinions exprimées est personnelle et ne peut en aucun cas être considérée comme représentative des points de vue du Ministère de l’Économie et des Finances ou de tout autres entités mentionnées dans ce document de travail.  \nDisclaimer. The opinions expressed in this working paper are the sole responsibility of the authors and do not reflect the views of the Ministry of Economy and Finance or any other mentioned entities.  \n1 Introduction  \nIn economic context, nowcasting refers to the ability to estimate current Gross Domestic products (GDP) before data official release. This technique relies on a diverse set of high frequency indicators and other realtime economic variables to generate rapid and accurate estimates [25, 29] . It enables policymakers and researchers to gain insights into current economic conditions, especially in situations where official data maybe incomplete or subject to delays.  \nMachine Learning algorithms have now become a valuable tool in economic modelling, demonstrating remarkable efficacy in the challenging task of nowcasting and forecasting GDP across diverse global contexts. This effectiveness is evident in advanced economies (e.g., Canada [54], China [67, 69], Finland [26], Italy [21], Netherlands [42], New Zealand [55, 56, 60], South Africa [17], Sweden [40], USA [31, 45], multiple European countries [23]), emerging markets and developing countries (e.g., Albania [66], Bangladesh [32], Belize and El Savador [5], Brazil [57], Egypt [1], Georgia [46], India [28], Indonesia [62], Lebanon [64], Malaysia [38], Peru [63])) . Moreover, Machine learning algorithms are also proved to be very competitive with respect to standard econometric methods.  \nFor Madagascar particularly, the task of nowcasting is more challenging due to the","cbCaiictuCJ6iMIL","https://ap.wps.com/l/cbCaiictuCJ6iMIL","pdf",477537,1,14,"English","en",105,"# Abstract\n# Introduction\n## Nowcasting and economic context\n## Machine learning in GDP nowcasting\n# Experimental setup\n## Dataset and features","[{\"question\":\"Which machine learning algorithms are evaluated for nowcasting Madagascar’s real GDP?\",\"answer\":\"The paper tests regularized linear models (Ridge, Lasso, Elastic-net), principal component regression, k-NN regression, linear SVR, and tree-based ensembles using Random Forest and XGBoost.\"},{\"question\":\"What data and time period are used in the study?\",\"answer\":\"The models are trained on 10 Malagasy quarterly macroeconomic leading indicators covering 2007Q1–2022Q4.\"},{\"question\":\"How is model performance measured?\",\"answer\":\"Accuracy is compared using RMSE, MAE, and MAPE computed for each nowcasting model.\"}]","Nowcasting Madagascar’s real GDP using machine learning algorithms | PDF",1785721440,35,{"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},"nowcasting-madagascars-real-gdp-using-machine-learning-algorithms","",{"@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/nowcasting-madagascars-real-gdp-using-machine-learning-algorithms/118988/",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","2026-08-03",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},"Which machine learning algorithms are evaluated for nowcasting Madagascar’s real GDP?","Question",{"text":75,"@type":76},"The paper tests regularized linear models (Ridge, Lasso, Elastic-net), principal component regression, k-NN regression, linear SVR, and tree-based ensembles using Random Forest and XGBoost.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data and time period are used in the study?",{"text":80,"@type":76},"The models are trained on 10 Malagasy quarterly macroeconomic leading indicators covering 2007Q1–2022Q4.",{"name":82,"@type":73,"acceptedAnswer":83},"How is model performance measured?",{"text":84,"@type":76},"Accuracy is compared using RMSE, MAE, and MAPE computed for each nowcasting model.","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"]