Working Paper: CEPR ID: DP7895
Authors: Janine Aron; John Muellbauer
Abstract: Inflation is a far from homogeneous phenomenon, a fact often neglected in modelling consumer price inflation. This study, the first of its kind for an emerging market country, investigates gains to inflation forecast accuracy by aggregating weighted forecasts of the sub-component price indices, versus forecasting the aggregate consumer price index itself. Rich multivariate equilibrium correction models employ general and sectoral information for ten sub-components, taking account of structural breaks and institutional changes. Model selection is over 1979-2003, with pseudo out-of-sample forecasts, four-quarters-ahead, generated to 2007. Aggregating the weighted forecasts of the sub-components does outperform the aggregate CPI forecasts, and also offers substantial gains over forecasting using benchmark naïve models. The analysis also contributes an improved understanding of sectoral inflationary pressures. This forecasting method should be more robust to the regular reweighting of the CPI index.
Keywords: CPI; subcomponents; disaggregation; error correction models; evaluating forecasts; model selection; multivariate time series; sectoral inflation
JEL Codes: C22; C32; C51; C52; C53; E31; E52
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Aggregating weighted forecasts of subcomponent price indices (C43) | Improved inflation forecast accuracy (E31) |
| Weighted forecasts of subcomponent price indices (C43) | Mitigation of individual forecast errors (C53) |
| Mitigation of individual forecast errors (C53) | More accurate overall CPI forecast (C43) |
| Aggregating weighted forecasts of subcomponent price indices (C43) | Outperformance of naive models (C52) |