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Econometric modelling for short-term inflation forecasting in the euro area


Author Info

  • Antoni Espasa

    (Departamento de Estad�stica, Universidad Carlos III de Madrid, Spain)

  • Rebeca Albacete

    (Departamento de Econom�a Aplicada, Universidad Autónoma de Madrid)

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    This paper examines the problem of forecasting macro-variables which are observed monthly (or quarterly) and result from geographical and sectorial aggregation. The aim is to formulate a methodology whereby all relevant information gathered in this context could provide more accurate forecasts, be frequently updated, and include a disaggregated explanation as useful information for decision-making. The appropriate treatment of the resulting disaggregated data set requires vector modelling, which captures the long-run restrictions between the different time series and the short-term correlations existing between their stationary transformations. Frequently, due to a lack of degrees of freedom, the vector model must be restricted to a block-diagonal vector model. This methodology is applied in this paper to inflation in the euro area, and shows that disaggregated models with cointegration restrictions improve accuracy in forecasting aggregate macro-variables. Copyright © 2007 John Wiley & Sons, Ltd.

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    Bibliographic Info

    Article provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.

    Volume (Year): 26 (2007)
    Issue (Month): 5 ()
    Pages: 303-316

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    Handle: RePEc:jof:jforec:v:26:y:2007:i:5:p:303-316

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    Cited by:
    1. Hernandez Martinez, Fernando, 2009. "Efectos del incremento del precio del petróleo en la economía española: Análisis de cointegración y de la política monetaria mediante reglas de Taylor
      [Oil price shocks and the spanish econom
      ," MPRA Paper 18056, University Library of Munich, Germany.
    2. Guillermo Carlomagnol & Antoni Espasa, 2014. "The pairwise approach to model a large set of disaggregates with common trends," Statistics and Econometrics Working Papers ws141309, Universidad Carlos III, Departamento de Estadística y Econometría.


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