Kingdom of Lesotho: Selected Issues

International Monetary Fund

No 2024/289, IMF Staff Country Reports from International Monetary Fund

Abstract: This Selected Issues paper delves into few applications of machine learning (ML), with a particular application to economic forecasts in Lesotho. Amid delayed and often revised gross domestic product data, this paper explores the potential of ML to provide real-time insights into growth and inflation trends, crucial for informed policymaking. By leveraging nontraditional data and employing a variety of ML models, the paper presents a comprehensive analysis of current economic activity, evaluates the accuracy of standard statistical measures, and forecasts future inflation trends. The findings underscore the efficacy of ML in reducing prediction errors and highlight the significant role of alternative data in circumventing the limitations posed by traditional economic indicators. This paper contributes to the broader debate on the application of advanced computational techniques in economic forecasting, offering valuable insights for policymakers in Lesotho and similar countries grappling with data constraints and the need for timely economic analysis.

Keywords: E. inflation forecasting; GDP Nowcasting result; D. GDP evaluation; C. GDP Nowcasting; ensemble Nowcasting model; Machine learning; Inflation; Africa; Global (search for similar items in EconPapers)
Pages: 18
Date: 2024-09-11
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.imf.org/external/pubs/cat/longres.aspx?sk=554738 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:imf:imfscr:2024/289

Ordering information: This working paper can be ordered from
http://www.imf.org/external/pubs/pubs/ord_info.htm

Access Statistics for this paper

More papers in IMF Staff Country Reports from International Monetary Fund International Monetary Fund, Washington, DC USA. Contact information at EDIRC.
Bibliographic data for series maintained by Akshay Modi ().