Measures of Underlying Inflation in the Euro Area: Assessment and Role for Informing Monetary Policy
IMF Working Papers, August 1, 2006
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Bibliographic details
- Authors: Emil Stavrev
- Published: August 1, 2006
- Series: IMF Working Papers
- DOI: https://doi.org/10.5089/9781451864571.001
Overview
- Evaluates the 24-month ahead inflation forecasting performance of various indicators of underlying inflation and structural models.
- Emphasizes implications for the monetary policy horizon and usefulness as leading indicators of headline inflation.
Key Findings
- Inflation forecast errors resulting from model misspecification are larger than the errors resulting from forecasting of exogenous variables.
- Measures derived using the generalized dynamic factor model (GDFM) overperform other measures over the monetary policy horizon and are leading indicators of headline inflation.
- Trimmed means, although weaker than GDFM indicators, have good forecasting performance.
- Indicators by permanent exclusion underperform but provide useful information about short-term dynamics.
- The forecasting performance of theoretically-founded models that relate monetary aggregates, the output gap, and inflation improves with the time horizon but generally falls short of that of the GDFM.
- A composite measure of underlying inflation, derived by averaging the statistical indicators and the model-based estimates, improves forecast accuracy by eliminating bias and offers valuable insight about the distribution of risks.
Methodology and Models Assessed
- Statistical indicators including GDFM-based measures.
- Trimmed means and indicators by permanent exclusion.
- Theoretically-founded structural models linking monetary aggregates, the output gap, and inflation.
- Evaluation focused on 24-month ahead forecasts and performance over the monetary policy horizon.
Forecasting Performance Insights
- GDFM indicators: best-performing across the monetary policy horizon and act as leading indicators of headline inflation.
- Trimmed means: good performance though weaker than GDFM.
- Permanent exclusion indicators: weaker forecasting performance but useful for short-term dynamics.
- Structural/theoretical models: performance improves with horizon length but generally underperform GDFM.
- Model misspecification risk: the dominant source of inflation forecast error relative to errors from forecasting exogenous variables.
Composite Measure Recommendation
- Constructing a composite measure by averaging statistical indicators and model-based estimates:
- Improves forecast accuracy by eliminating bias.
- Provides valuable insight about the distribution of risks.