## Guatemala: Technical Assistance Report-The Statistical Component of Liquidity Forecasting

_Technical Assistance Reports, January 29, 2024_

## Source details

**Canonical URL:** [Guatemala: Technical Assistance Report-The Statistical Component of Liquidity Forecasting](https://www.imf.org/en/publications/technical-assistance-reports/issues/2024/01/26/guatemala-technical-assistance-report-the-statistical-component-of-liquidity-forecasting-544142)

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## Bibliographic details
- Published: January 29, 2024
- Series: Technical Assistance Reports
- DOI: https://doi.org/10.5089/9798400266904.019

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### Mission overview and objectives
- A Technical Assistance (TA) mission was conducted in Guatemala City, Guatemala, from June 12 to 16, 2023.
- Purpose: assist the authorities with the statistical component of liquidity management.
- Focus areas:
  - Time series analysis of the demand for liquidity.
  - Analysis of autonomous factors affecting liquidity.
  - Addressing forecasting challenges arising from peculiarities in the set-up of the reserve requirement.

### Methodology and model structure
- Forecasting approach:
  - Forecast each deposit type of the reserve requirement base separately.
  - Forecast the demand for excess reserves by banks.
  - Apply statistical reconciliation to obtain an aggregated forecast from component forecasts.
- Rationale:
  - Peculiarities in reserve requirement design motivate disaggregated forecasting by deposit type.
  - Statistical reconciliation improves forecast accuracy by reconciling individual-component forecasts with the aggregate.
- Capacity building:
  - A three-day workshop was conducted during the mission on running the model to support institutionalization.

### Key findings and results
- Statistical reconciliation improves forecast accuracy (statement from the report).
- One-day horizon forecasts exhibit limited forecast errors and are suitable for operational publication to market participants.
- The report provides a detailed summary of the technical components of the liquidity forecasting model to guide routine use by authorities.

### Policy recommendations and operational guidance
- Use the statistical forecast to complement institutional information and to check the quality of information directly obtained from counterparties.
- Publish forecasts beginning with the one-day horizon to help banks formulate better-informed bidding at the daily deposit operations.
- Institutionalize the liquidity forecasting model and routine processes supported by the technical documentation and the on-site workshop.

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_Source: https://www.imf.org/en/publications/technical-assistance-reports/issues/2024/01/26/guatemala-technical-assistance-report-the-statistical-component-of-liquidity-forecasting-544142_