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Activity Number:
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344
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Type:
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Invited
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Date/Time:
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Tuesday, August 4, 2009 : 2:00 PM to 3:50 PM
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Sponsor:
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Business and Economic Statistics Section
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| Abstract - #303106 |
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Title:
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Forecasting Inflation with Gradual Regime Shifts and Exogenous Information
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Author(s):
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Kistin Hubrich*+ and Timo Teraesvirta and Andrés González
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Companies:
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European Central Bank and Aarhus University and Central Bank of Colombia
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Address:
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Kaiserstrasse 29, Frankfurt am Main, D-60311, Germany
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Keywords:
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nonlinear forecast ; nonlinear model ; nonlinear trend ; penalised likelihood ; structural shift ; time-varying parameter
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Abstract:
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We make use of the shifting-mean autoregressive 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. 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. We show, both theoretically and by simulations, how this is done by using the penalized likelihood in the estimation of model parameters. 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 does improve the forecast accuracy compared to that of a linear autoregressive benchmark model.
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