[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119037-en":3,"doc-seo-119037-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},119037,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Nowcasting Madagascar’s real GDP using machine learning algorithms","This working paper evaluates how different machine learning regression algorithms can nowcast Madagascar’s real gross domestic product (GDP) before official releases. Models trained on 10 quarterly leading macroeconomic indicators for 2007Q1–2022Q4 include Ridge, Lasso, Elastic-net, principal component regression, k-NN, linear SVR, Random Forest, and XGBoost, benchmarked against econometric approaches. Accuracy is assessed via RMSE, MAE, and MAPE. Results show that an Ensemble aggregation consistently outperforms traditional econometric models, supporting timely, data-driven guidance for policymaking.","Nowcasting Madagascar’s real GDP using machine learning algorithms  \nFranck Ramaharo1 and Gerzhino 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  \narXiv :2401 . 10255v1 [ econ .GN] 24 Dec 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 scarcity of high frequency indicators as well as their relatively short timespan. In this study, we embrace this challenge by developing several machine learning models tailored to nowcast Madagascar’s real GDP. We take into account the variability in model performance and we adopt the forecast combination technique. This strategy aims to mitigate the risks associated with relying solely on individual models, as ","cbCaifT0KXopVszm","https://ap.wps.com/l/cbCaifT0KXopVszm","pdf",362767,1,13,"English","en",105,"# Introduction\n## Experimental setup\n### Dataset and features","[{\"question\":\"What does nowcasting mean in this study?\",\"answer\":\"Nowcasting refers to estimating the current GDP level before official data are released, using available high-frequency indicators and related variables.\"},{\"question\":\"Which machine learning models are evaluated for Madagascar’s GDP nowcasting?\",\"answer\":\"The study trains several regression models, including Ridge, Lasso, Elastic-net, principal component regression, k-NN regression, linear SVR, Random Forest, and XGBoost.\"},{\"question\":\"How is forecast accuracy measured and what is the main finding?\",\"answer\":\"Accuracy is measured using RMSE, MAE, and MAPE. The Ensemble model that aggregates individual predictions consistently delivers smaller errors than traditional econometric benchmarks.\"}]","Nowcasting Madagascar’s real GDP using machine learning algorithms | PDF",1785722036,33,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"nowcasting-madagascars-real-gdp-using-machine-learning-algorithms","",{"@graph":36,"@context":86},[37,54,69],{"@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/119037/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What does nowcasting mean in this study?","Question",{"text":76,"@type":77},"Nowcasting refers to estimating the current GDP level before official data are released, using available high-frequency indicators and related variables.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which machine learning models are evaluated for Madagascar’s GDP nowcasting?",{"text":81,"@type":77},"The study trains several regression models, including Ridge, Lasso, Elastic-net, principal component regression, k-NN regression, linear SVR, Random Forest, and XGBoost.",{"name":83,"@type":74,"acceptedAnswer":84},"How is forecast accuracy measured and what is the main finding?",{"text":85,"@type":77},"Accuracy is measured using RMSE, MAE, and MAPE. 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