Department of Economics and Business Economics

Timo Teräsvirta

Forecasting inflation with gradual regime shifts and exogenous information

Research output: Working paperResearch


  • Rp09 03

    Final published version, 426 KB, PDF-document

    Andrés González, Banco de la República, ColombiaKirstin Hubrich, European Central Bank, Frankfurt am Main, Germany
  • Timo Teräsvirta
  • School of Economics and Management
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.
Original languageEnglish
Place of publicationAarhus
PublisherInstitut for Økonomi, Aarhus Universitet
Number of pages30
Publication statusPublished - 2009

    Research areas

  • Nonlinear forecast, nonlinear model, nonlinear trend, penalised likelihood, structural shift, time-varying parameter

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