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Real-time inflation forecasting in a changing world

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  • Groen, J.J.J.
  • Paap, R.

Abstract

This paper revisits inflation forecasting using reduced form Phillips curve forecasts, i.e., inflation forecasts using activity and expectations variables. We propose a Phillips curve-type model that results from averaging across different regression specifications selected from a set of potential predictors. The set of predictors includes lagged values of inflation, a host of real activity data, term structure data, nominal data and surveys. In each of the individual specifications we allow for stochastic breaks in regression parameters, where the breaks are described as occasional shocks of random magnitude. As such, our framework simultaneously addresses structural change and model certainty that unavoidably affects Phillips curve forecasts. We use this framework to describe PCE deflator and GDP deflator inflation rates for the United States across the post-WWII period. Over the full1960-2008 sample the framework indicates several structural breaks across different combinations of activity measures. These breaks often coincide with, amongst others, policy regime changes and oil price shocks. In contrast to many previous studies, we find less evidence for autonomous variance breaks and inflation gap persistence. Through a \\textit{real-time} out-of-sample forecasting exercise we show that our model specification generally provides superior one-quarter and one-year ahead forecasts for quarterly inflation relative to a whole range of forecasting models that are typically used in the literature.

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File URL: http://hdl.handle.net/1765/16709
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Bibliographic Info

Paper provided by Erasmus University Rotterdam, Econometric Institute in its series Econometric Institute Report with number EI 2009-19.

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Date of creation: 10 Sep 2009
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Handle: RePEc:dgr:eureir:1765016709

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Web page: http://www.few.eur.nl/few

Related research

Keywords: Bayesian model averaging; structural breaks; real-time data; model uncertainty; Phillips correlations; inflation forecasting;

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  1. Christine De Mol & Domenico Giannone & Lucrezia Reichlin, 2006. "Forecasting using a large number of predictors - Is Bayesian regression a valid alternative to principal components?," Working Paper Series 700, European Central Bank.
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