Econometric Modelling For Short-Term Inflation Forecasting In The Emu
Inflation forecasts are in great demand by agents in financial markets and monetary authorities that also require frequent updates. In the case of the EMU, these can be done monthly using Harmonised Indices of Consumer Prices (HICP). Analysing the HICP it was detected in a previous paper that breaking down the HICP in a vector of n sectors so that each price index component corresponds to a group of relatively homogeneous markets, or in a vector of n countries, there are in both cases fewer than (n-1) cointegration relationships. It can then be said that the components of the index are not fully cointegrated in the sense that there is more than one common trend in the HICP vector. In such a case, one way to increase sample information about the HICP trend is to consider the n price components and approach disaggregated econometric modelling. The paper shows that the breakdown that joins both criteria by considering a price index for each large group of markets in each country improves EMU inflation forecasts and establishes a framework in which general and specific explanatory variables and non-linear structures can be introduced for further improvements. The paper shows that VEqCM of ten price indices – two sectors by five geographical areas – including three cointegration relationships, with a sector-block diagonal restriction, generates forecasts of the year-on-year inflation rate in the HICP such that their error variances are one third or one fifth of the forecast errors from an aggregate ARIMA model, depending whether the horizon is three or twelve months. This vector model also provides better forecasts than single-equation models or alternative vector models for the components. A successful formulation of the vector model requires the inclusion of dummy variables to take account of special events such as seasonality changes due to sales, the introduction of the euro, Greece becoming a member of the EMU, the introduction of ecological taxes, bad weather periods and others events altering the evolution of unprocessed food prices, etc. and the inclusion of international Brent prices in euros. With the breakdown used in the paper it is shown that a usual measure of core inflation is not a good predictor of total inflation, but the interest in core inflation could lie in the fact that its corresponding price index is constructed with price indices in which innovations are more persistent than those in the other consumer price indexes excluded from the core. The disaggregated forecasts presented in this paper are useful for policy-making because they tell us which sectors have the highest expected inflation rates and how persistent are the shocks affecting different sectors.
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