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Forecasting Inflation In The Euro Area Using Monthly Time Series Models And Quarterly Econometric Models

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  • Rebeca Albacete

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  • Antoni Espasa

    ()

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    Abstract

    Economic agents and financial authorities require frequent updates to a path of accurate inflation forecasts and need forecasts to include an explanation of the factors by which they are determined. This paper studies how to approach this need, developing a method for analysing inflation in the euro area, measured according to HICP. Time series models using the most recent information on prices and an important functional and geographically disaggregation can provide monthly forecasts which are reasonably accurate, but they do not provide an explanation of the factors by which the forecast is determined. In this respect, it is important to enlarge the data set used considering explanatory variables and build congruent econometric models including variables which, following previous works by D. Hendry, capture disequilibria on different markets, goods and services, labour, monetary and international. The final result of this work shows that combining the forecasts from a monthly time series vector model, constructed on price subindexes from a disaggregation of the HICP by countries and sectors, with the forecasts derived from a quarterly econometric vector model on aggregate inflation and other economic variables, very accurate forecasts are obtained. Both vector models are specified including empirical cointegration restrictions, which in the first case capture the constrains necessary present between the trends of the price subindexes and in the second approximate the long-run restrictions postulated by economic theory.

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    Bibliographic Info

    Paper provided by Universidad Carlos III, Departamento de Estadística y Econometría in its series Statistics and Econometrics Working Papers with number ws050401.

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    Date of creation: Jan 2005
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    Handle: RePEc:cte:wsrepe:ws050401

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    References

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    1. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2005. "Leading Indicators for Euro-area Inflation and GDP Growth," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(s1), pages 785-813, December.
    2. Benalal, Nicholai & Diaz del Hoyo, Juan Luis & Landau, Bettina & Roma, Moreno & Skudelny, Frauke, 2004. "To aggregate or not to aggregate? Euro area inflation forecasting," Working Paper Series 0374, European Central Bank.
    3. James G. MacKinnon, 2010. "Critical Values for Cointegration Tests," Working Papers 1227, Queen's University, Department of Economics.
    4. Jones, Charles I, 1995. "Time Series Tests of Endogenous Growth Models," The Quarterly Journal of Economics, MIT Press, vol. 110(2), pages 495-525, May.
    5. Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
    6. Ard Reijer & Peter Vlaar, 2006. "Forecasting Inflation: An Art as Well as a Science!," De Economist, Springer, vol. 154(1), pages 19-40, 03.
    7. Anindya Banerjee & Lynne Cockerell & Bill Russell, 2000. "An I(2) Analysis of Inflation and the Markup," Dundee Discussion Papers in Economics 120, Economic Studies, University of Dundee.
    8. Bowdler, Christopher & Jansen, Eilev S., 2004. "A markup model of inflation for the euro area," Working Paper Series 0306, European Central Bank.
    9. 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 1057-72, June.
    10. Jordi Gali & Mark Gertler & J. David Lopez-Salido, 2001. "European Inflation Dynamics," NBER Working Papers 8218, National Bureau of Economic Research, Inc.
    11. Kirstin Hubrich, 2004. "Forecasting euro area inflation: Does aggregating forecasts by HICP component improve forecast accuracy?," Computing in Economics and Finance 2004 230, Society for Computational Economics.
    12. Gordon de Brouwer & Neil R. Ericsson, 1995. "Modelling inflation in Australia," International Finance Discussion Papers 530, Board of Governors of the Federal Reserve System (U.S.).
    13. Antoni Espasa & Rebeca Albacete, 2004. "Econometric Modelling For Short-Term Inflation Forecasting In The Emu," Statistics and Econometrics Working Papers ws034309, Universidad Carlos III, Departamento de Estadística y Econometría.
    14. Dreger, Christian & Marcellino, Massimiliano, 2007. "A macroeconometric model for the Euro economy," Journal of Policy Modeling, Elsevier, vol. 29(1), pages 1-13.
    15. Jones, Charles I, 1995. "R&D-Based Models of Economic Growth," Journal of Political Economy, University of Chicago Press, vol. 103(4), pages 759-84, August.
    16. Johansen, Soren, 1991. "Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models," Econometrica, Econometric Society, vol. 59(6), pages 1551-80, November.
    17. Stock, James H. & Watson, Mark W., 1999. "Forecasting inflation," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 293-335, October.
    18. Granger, Clive W. J. & Jeon, Yongil, 2004. "Thick modeling," Economic Modelling, Elsevier, vol. 21(2), pages 323-343, March.
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    Cited by:
    1. 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.
    2. Aron, Janine & Muellbauer, John, 2012. "Improving forecasting in an emerging economy, South Africa: Changing trends, long run restrictions and disaggregation," International Journal of Forecasting, Elsevier, vol. 28(2), pages 456-476.

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