{
  "title": "Analyzing Determinants of Inflation When There Are Data Limitation: The Case of Sierra Leone",
  "publication": "IMF Working Papers, December 1, 2008",
  "sourceUrl": "https://www.imf.org/en/publications/wp/issues/2016/12/31/analyzing-determinants-of-inflation-when-there-are-data-limitation-the-case-of-sierra-leone-22520",
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  "summary": "This paper examines the determinants of inflation in Sierra Leone using a structural vector autoregression (VAR) approach to help forecast inflation for operational purposes. Despite data limitations, the paper accurately models inflation in Sierra Leone.",
  "sections": [
    {
      "heading": "Summary and Main Findings",
      "content": "- The paper examines the determinants of inflation in Sierra Leone using a structural vector autoregression (VAR) approach to help forecast inflation for operational purposes.\n- Despite data limitations, the paper accurately models inflation in Sierra Leone.\n- Domestic inflation is found to increase with:\n  - higher oil prices,\n  - higher money supply, and\n  - nominal exchange rate depreciation.\n- The paper employs a historical decomposition approach to pinpoint the sources of a marked decline in inflation in 2006 and assesses the model’s forecasting properties.\n- Overall conclusion: the model serves as a useful addition to the toolkit for analyzing and forecasting inflation in countries with limited data availability."
    },
    {
      "heading": "Methodology",
      "content": "- Structural vector autoregression (VAR) methodology is used to analyze determinants of inflation and to produce forecasts for operational purposes.\n- Historical decomposition is applied to identify the drivers of the marked decline in inflation in 2006."
    },
    {
      "heading": "Policy-Relevant Implications",
      "content": "- Modeling inflation with a structural VAR can be effective even when data availability is limited.\n- Key policy-relevant relationships identified:\n  - Oil price increases are associated with higher domestic inflation.\n  - Increases in money supply are associated with higher domestic inflation.\n  - Nominal exchange rate depreciation is associated with higher domestic inflation.\n- The model can inform operational forecasting and policy analysis in low-data environments.\n\n---\n\n Content in this bundle\n\n- wp08271\n  - wp08271 (Markdown version){rel=\"alternate\" type=\"text/markdown\"}\n  - wp08271 (PDF){rel=\"external\" type=\"application/pdf\"}\n\n---\n\nSource: https://www.imf.org/en/publications/wp/issues/2016/12/31/analyzing-determinants-of-inflation-when-there-are-data-limitation-the-case-of-sierra-leone-22520"
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    "[Markdown version](/en/publications/wp/issues/2016/12/31/analyzing-determinants-of-inflation-when-there-are-data-limitation-the-case-of-sierra-leone-22520/index.md)",
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    "Authors: Kadima D. Kalonji, Jan Gottschalk, Ken Miyajima",
    "Published: December 1, 2008",
    "Series: IMF Working Papers",
    "DOI: https://doi.org/10.5089/9781451871296.001",
    "The paper examines the determinants of inflation in Sierra Leone using a structural vector autoregression (VAR) approach to help forecast inflation for operational purposes.",
    "Despite data limitations, the paper accurately models inflation in Sierra Leone.",
    "Domestic inflation is found to increase with:",
    "The paper employs a historical decomposition approach to pinpoint the sources of a marked decline in inflation in 2006 and assesses the model’s forecasting properties.",
    "Overall conclusion: the model serves as a useful addition to the toolkit for analyzing and forecasting inflation in countries with limited data availability.",
    "Structural vector autoregression (VAR) methodology is used to analyze determinants of inflation and to produce forecasts for operational purposes.",
    "Historical decomposition is applied to identify the drivers of the marked decline in inflation in 2006.",
    "Modeling inflation with a structural VAR can be effective even when data availability is limited.",
    "Key policy-relevant relationships identified:",
    "The model can inform operational forecasting and policy analysis in low-data environments.",
    "**_wp08271**"
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