Forecasting Inflation Using Dynamic Model Averaging

Gary Koop, Dimitris Korobilis

Research output: Working paperDiscussion paper

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We forecast quarterly US inflation based on the generalized Phillips curve using econometric methods which incorporate dynamic model averaging. These methods not only allow for coefficients to change over time, but also allow for the entire forecasting model to change over time. We find that dynamic model averaging leads to substantial forecasting improvements over simple benchmark regressions and more sophisticated approaches such as those using time varying coefficient models. We also provide evidence on which sets of predictors are relevant for forecasting in each period.
Original languageEnglish
Place of PublicationGlasgow
PublisherUniversity of Strathclyde
Number of pages33
Publication statusPublished - Nov 2010


  • bayesian
  • state space model
  • Phillips curve


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