{
  "title": "Guatemala: Technical Assistance Report-The Statistical Component of Liquidity Forecasting",
  "publication": "Technical Assistance Reports, January 29, 2024",
  "sourceUrl": "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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  "summary": "A Technical Assistance (TA) mission was conducted in Guatemala City, Guatemala, from June 12 to 16, 2023, to assist the authorities with the statistical component of liquidity management. The TA mission focused on time series analysis of the demand for liquidity and autonomous factors.",
  "sections": [
    {
      "heading": "Mission overview and objectives",
      "content": "- A Technical Assistance (TA) mission was conducted in Guatemala City, Guatemala, from June 12 to 16, 2023.\n- Purpose: assist the authorities with the statistical component of liquidity management.\n- Focus areas:\n  - Time series analysis of the demand for liquidity.\n  - Analysis of autonomous factors affecting liquidity.\n  - Addressing forecasting challenges arising from peculiarities in the set-up of the reserve requirement."
    },
    {
      "heading": "Methodology and model structure",
      "content": "- Forecasting approach:\n  - Forecast each deposit type of the reserve requirement base separately.\n  - Forecast the demand for excess reserves by banks.\n  - Apply statistical reconciliation to obtain an aggregated forecast from component forecasts.\n- Rationale:\n  - Peculiarities in reserve requirement design motivate disaggregated forecasting by deposit type.\n  - Statistical reconciliation improves forecast accuracy by reconciling individual-component forecasts with the aggregate.\n- Capacity building:\n  - A three-day workshop was conducted during the mission on running the model to support institutionalization."
    },
    {
      "heading": "Key findings and results",
      "content": "- Statistical reconciliation improves forecast accuracy (statement from the report).\n- One-day horizon forecasts exhibit limited forecast errors and are suitable for operational publication to market participants.\n- The report provides a detailed summary of the technical components of the liquidity forecasting model to guide routine use by authorities."
    },
    {
      "heading": "Policy recommendations and operational guidance",
      "content": "- Use the statistical forecast to complement institutional information and to check the quality of information directly obtained from counterparties.\n- Publish forecasts beginning with the one-day horizon to help banks formulate better-informed bidding at the daily deposit operations.\n- Institutionalize the liquidity forecasting model and routine processes supported by the technical documentation and the on-site workshop.\n\n---\n\n Content in this bundle\n\n- Tarea2024002\n  - Tarea2024002 (Markdown version){rel=\"alternate\" type=\"text/markdown\"}\n  - Tarea2024002 (PDF){rel=\"external\" type=\"application/pdf\"}\n\n---\n\nSource: 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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    "Published: January 29, 2024",
    "Series: Technical Assistance Reports",
    "DOI: https://doi.org/10.5089/9798400266904.019",
    "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:",
    "Forecasting approach:",
    "Rationale:",
    "Capacity building:",
    "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.",
    "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.",
    "**Tarea2024002**"
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