Forecasting Ination Using Dynamic Model Averaging
نویسندگان
چکیده
We forecast quarterly US ination based on the generalized Phillips curve using econometric methods which incorporate dynamic model averaging. These methods not only allow for coe¢ cients to change over time, but also allow for the entire forecasting model to change over time. We nd that dynamic model averaging leads to substantial forecasting improvements over simple benchmark regressions and more sophisticated approaches such as those using time varying coe¢ cient models. We also provide evidence on which sets of predictors are relevant for forecasting in each period. Keywords: Bayesian, State space model, Phillips curve JEL Classi cation: E31, E37, C11, C53 Both authors are Fellows at the Rimini Centre for Economic Analysis. Address for correspondence: Gary Koop, Department of Economics, University of Strathclyde, 130 Rottenrow, Glasgow G4 0GE, UK. Email: [email protected]
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