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Econometric modelling for short-term inflation forecasting in the EMU


  • Espasa, Antoni
  • Albacete, Rebeca


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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  • Espasa, Antoni & Albacete, Rebeca, 2004. "Econometric modelling for short-term inflation forecasting in the EMU," DES - Working Papers. Statistics and Econometrics. WS ws034309, Universidad Carlos III de Madrid. Departamento de Estadística.
  • Handle: RePEc:cte:wsrepe:ws034309

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    References listed on IDEAS

    1. Anindya Banerjee & Lynne Cockerell & Bill Russell, 2001. "An I(2) analysis of inflation and the markup," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(3), pages 221-240.
    2. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-1072, June.
    3. A. Espasa & E. Senra & R. Albacete, 2002. "Forecasting inflation in the European Monetary Union: A disaggregated approach by countries and by sectors," The European Journal of Finance, Taylor & Francis Journals, vol. 8(4), pages 402-421.
    4. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    5. de Brouwer, Gordon & Ericsson, Neil R, 1998. "Modeling Inflation in Australia," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(4), pages 433-449, October.
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    Cited by:

    1. Aron, Janine & Muellbauer, John & Sebudde, Rachel, 2015. "Inflation forecasting models for Uganda: is mobile money relevant?," CEPR Discussion Papers 10739, C.E.P.R. Discussion Papers.
    2. Janine Aron & John Muellbauer & Coen Pretorius, 2004. "A Framework for Forecasting the Components of the Consumer Price," Development and Comp Systems 0409054, EconWPA.
    3. Espasa, Antoni & Albacete, Rebeca, 2005. "Forecasting inflation in the euro area using monthly time series models and quarterly econometric models," DES - Working Papers. Statistics and Econometrics. WS ws050401, Universidad Carlos III de Madrid. Departamento de Estadística.
    4. Janine Aron & John Muellbauer, 2008. "New methods for forecasting inflation and its sub-components: application to the USA," Economics Series Working Papers 406, University of Oxford, Department of Economics.
    5. Espasa, Antoni & Tena Horrillo, Juan de Dios & Pino, Gabriel, 2008. "Forecasting Spanish inflation using information from different sectors and geographical areas," DES - Working Papers. Statistics and Econometrics. WS ws080101, Universidad Carlos III de Madrid. Departamento de Estadística.
    6. Espasa, Antoni & Tena Horrillo, Juan de Dios & Pino, Gabriel, 2013. "Forecasting disaggregates by sectors and regions : the case of inflation in the euro area and Spain," DES - Working Papers. Statistics and Econometrics. WS ws130807, Universidad Carlos III de Madrid. Departamento de Estadística.
    7. Robinson Durán & Evelyn Garrido & Carolina Godoy & Juan de Dios Tena, 2012. "Predicción de la inflación en México con modelos desagregados por componente," Estudios Económicos, El Colegio de México, Centro de Estudios Económicos, vol. 27(1), pages 133-167.
    8. Aron, Janine & Muellbauer, John, 2010. "Does aggregating forecasts by CPI component improve inflation forecast accuracy in South Africa?," CEPR Discussion Papers 7895, C.E.P.R. Discussion Papers.
    9. Juan de Dios TENA & Antoni ESPASA & Gabriel PINO, "undated". "Forecasting Inflation and Relative Prices in the European Regions: A Case Study," Regional and Urban Modeling 284100040, EcoMod.

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