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Forecasting inflation with gradual regime shifts and exogenous information

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Author Info

  • Andrés González

    (Banco de la República, Bogotá and CREATES, University of Aarhus, Denmark)

  • Kirstin Hubrich

    (European Central Bank, Frankfurt am Main and CREATES, University of Aarhus, Denmark)

  • Timo Teräsvirta

    ()
    (CREATES, University of Aarhus, Denmark)

Abstract

In this work, we make use of the shifting-mean autoregressive model which is a flexible univariate nonstationary model. It is suitable for describing characteristic features in inflation series as well as for medium-term forecasting. With this model we decompose the inflation process into a slowly moving nonstationary component and dynamic short-run fluctuations around it. We fit the model to the monthly euro area, UK and US inflation series. An important feature of our model is that it provides a way of combining the information in the sample and the a priori information about the quantity to be forecast to form a single inflation forecast. We show, both theoretically and by simulations, how this is done by using the penalised likelihood in the estimation of model parameters. In forecasting inflation, the central bank inflation target, if it exists, is a natural example of such prior information. We further illustrate the application of our method by an ex post forecasting experiment for euro area and UK inflation. We find that that taking the exogenous information into account does im- prove the forecast accuracy compared to that of a linear autoregressive benchmark model.

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Bibliographic Info

Paper provided by School of Economics and Management, University of Aarhus in its series CREATES Research Papers with number 2009-03.

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Length: 30
Date of creation: 28 Jan 2009
Date of revision:
Handle: RePEc:aah:create:2009-03

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Web page: http://www.econ.au.dk/afn/

Related research

Keywords: Nonlinear forecast; nonlinear model; nonlinear trend; penalised likelihood; structural shift; time-varying parameter;

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References

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Citations

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Cited by:
  1. Baillie, Richard T. & Morana, Claudio, 2012. "Adaptive ARFIMA models with applications to inflation," Economic Modelling, Elsevier, Elsevier, vol. 29(6), pages 2451-2459.
  2. Fabian Krueger & Frieder Mokinski & Winfried Pohlmeier, 2011. "Combining Survey Forecasts and Time Series Models: The Case of the Euribor," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), Justus-Liebig University Giessen, Department of Statistics and Economics, Justus-Liebig University Giessen, Department of Statistics and Economics, vol. 231(1), pages 63-81, February.
  3. Barbara Rossi, 2011. "Advances in Forecasting Under Instability," Working Papers, Duke University, Department of Economics 11-20, Duke University, Department of Economics.
  4. Claudio Morana, 2013. "Factor Vector Autoregressive Estimation of Heteroskedastic Persistent and Non Persistent Processes Subject to Structural Breaks: New Insights on the US OIS SPreads Term Structure," Working Papers, University of Milano-Bicocca, Department of Economics 233, University of Milano-Bicocca, Department of Economics, revised Feb 2013.
  5. Matthew T. Holt & Timo Teräsvirta, 2012. "Global Hemispheric Temperature Trends and Co–Shifting: A Shifting Mean Vector Autoregressive Analysis," CREATES Research Papers, School of Economics and Management, University of Aarhus 2012-54, School of Economics and Management, University of Aarhus.

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