## Kingdom of Lesotho: Selected Issues

_IMF Staff Country Reports, September 11, 2024_

## Source details

**Canonical URL:** [Kingdom of Lesotho: Selected Issues](https://www.imf.org/en/publications/cr/issues/2024/09/10/kingdom-of-lesotho-selected-issues-554738)

## Other formats

- [Markdown version](/en/publications/cr/issues/2024/09/10/kingdom-of-lesotho-selected-issues-554738/index.md)
- [Structured JSON version](/en/publications/cr/issues/2024/09/10/kingdom-of-lesotho-selected-issues-554738/index.json)
- [Bundle manifest](/en/publications/cr/issues/2024/09/10/kingdom-of-lesotho-selected-issues-554738/bundle-manifest.json)

## Bibliographic details
- Published: September 11, 2024
- Series: IMF Staff Country Reports
- DOI: https://doi.org/10.5089/9798400287947.002

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### Overview and scope
- Publication date: September 11, 2024
- This Selected Issues paper examines applications of machine learning (ML) with a particular application to economic forecasts in Lesotho.
- Motivation: delayed and often revised gross domestic product data that limit timely policymaking.
- Objective: explore the potential of ML to provide real-time insights into growth and inflation trends by leveraging nontraditional data and a variety of ML models.

### Major findings
- ML models can provide comprehensive analysis of current economic activity and forecasts of future inflation trends.
- The findings underscore the efficacy of ML in reducing prediction errors relative to standard approaches.
- Alternative (nontraditional) data play a significant role in circumventing limitations posed by traditional economic indicators.
- The paper evaluates the accuracy of standard statistical measures in the Lesotho context and contrasts them with ML-based approaches.

### Methodology highlights
- Use of nontraditional data sources to inform real-time growth and inflation assessments.
- Employment of a variety of ML models, including ensemble approaches, to produce nowcasts and forecasts.
- Comparative evaluation of ML-based nowcasting and inflation forecasting against standard statistical measures.

### Policy implications and recommendations
- ML-based tools can strengthen real-time monitoring of economic activity where official data are delayed or frequently revised.
- Incorporating alternative data sources into official monitoring frameworks can improve the timeliness and accuracy of growth and inflation signals.
- Policymakers in Lesotho and similar countries should consider integrating advanced computational techniques into their analytical toolkits to support informed decision-making under data constraints.

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## Content in this bundle

- **Kingdom of Lesotho: Selected Issues; IMF Country Report No. 24/289; August 22, 2024**
  - [Kingdom of Lesotho: Selected Issues; IMF Country Report No. 24/289; August 22, 2024 (Markdown version)](/-/media/files/publications/cr/2024/english/1lsoea2024002-print-pdf.pdf.md){rel="alternate" type="text/markdown"}
  - [Kingdom of Lesotho: Selected Issues; IMF Country Report No. 24/289; August 22, 2024 (PDF)](/-/media/files/publications/cr/2024/english/1lsoea2024002-print-pdf.pdf){rel="external" type="application/pdf"}

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_Source: https://www.imf.org/en/publications/cr/issues/2024/09/10/kingdom-of-lesotho-selected-issues-554738_