Forecasting Inflation Using Dynamic Model Averaging
There is a large literature on forecasting inflation using the generalized Phillips curve (i.e. using forecasting models where inflation depends on past inflation, the unemployment rate and other predictors). The present paper extends this literature through the use of econometric methods which incorporate dynamic model averaging. These not only allow for coefficients to change over time (i.e. the marginal effect of a predictor for inflation can change), but also allows for the entire forecasting model to change over time (i.e. different sets of predictors can be relevant at different points in time). In an empirical exercise involving quarterly US inflation, we find that dynamic model averaging leads to substantial forecasting improvements over simple benchmark approaches (e.g. random walk or recursive OLS forecasts) and more sophisticated approaches such as those using time varying coefficient models.
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