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Forecasting inflation with gradual regime shifts and exogenous information Author info | Abstract | Publisher info | Download info | Related research | Statistics 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)
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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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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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Date of creation: 28 Jan 2009Date of revision:
Handle: RePEc:aah:create:2009-03Contact details of provider: Web page: http://www.econ.au.dk/afn/
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Keywords: Nonlinear forecast ; nonlinear model ; nonlinear trend ; penalised likelihood ; structural shift ; time-varying parameter ; Find related papers by JEL classification: C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation and Testing C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation
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