Analyzing Determinants of Inflation When There Are Data Limitation: The Case of Sierra Leone
IMF Working Papers, December 1, 2008
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Bibliographic details
- Authors: Kadima D. Kalonji, Jan Gottschalk, Ken Miyajima
- Published: December 1, 2008
- Series: IMF Working Papers
- DOI: https://doi.org/10.5089/9781451871296.001
Summary and Main Findings
- 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:
- higher oil prices,
- higher money supply, and
- nominal exchange rate depreciation.
- 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.
Methodology
- 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.
Policy-Relevant Implications
- Modeling inflation with a structural VAR can be effective even when data availability is limited.
- Key policy-relevant relationships identified:
- Oil price increases are associated with higher domestic inflation.
- Increases in money supply are associated with higher domestic inflation.
- Nominal exchange rate depreciation is associated with higher domestic inflation.
- The model can inform operational forecasting and policy analysis in low-data environments.