Real-Time Inflation Forecasting in a Changing World

• Activity Number: 20
• Type: Topic Contributed
• Date/Time: Sunday, July 31, 2011 : 2:00 PM to 3:50 PM
• Sponsor: Business and Economic Statistics Section
• Author(s): Jan J. J. Groen*+ and Richard Paap and Francesco Ravazzolo
• Companies: Federal Reserve Bank of New York and Erasmus University and Norges Bank
• Keywords: Inflation forecasting ; Phillips correlations ; real-time data ; structural breaks ; model uncertainty ; Bayesian model averaging

Abstract

This paper revisits inflation forecasting using reduced form Phillips curve forecasts, i.e., inflation forecasts using activity and expectations variables. We propose a model that results from averaging across different regression specifications selected from a set of potential predictors that includes inflation lags, different real activity data, term structure data, nominal data and surveys. In each of the individual specifications we allow for occasional shocks of random magnitude in the regression parameters. As such, our framework simultaneously addresses structural change and model uncertainty that unavoidably affects Phillips curve forecasts. Over the full 1960-2008 sample the framework indicates several structural breaks across different combinations of activity measures, which often coincide with, amongst others, policy regime changes and oil price shocks. We find less evidence than elsewhere in the literature for variance breaks and inflation gap persistence. In a real-time out-of-sample forecasting exercise our model specification generally provides superior one-quarter and one-year ahead forecasts for quarterly inflation relative to a range of regularly used models.