Real-Time Inflation Forecasting in a Changing World
نویسندگان
چکیده
Abstract This paper revisits real-time forecasting of U.S. inflation based on Phillips curve-inspired linear regression models. Our innovation is to allow for both structural breaks in the regression parameters and the variance as well as uncertainty regarding which set of predictor variables one can include in these regressions (‘model uncertainty’). Structural breaks are described by occasional shocks of random magnitude. The set of potential predictors includes lagged values of inflation, output series, interest rate series and money. Parameter estimation and forecasting are performed using a Gibbs sampling approach with Bayesian model averaging. We compare our approach with many alternative univariate and multivariate model specifications including a random walk model. Posterior results show that our model specification provides superior 1-step ahead and 4-step ahead forecasts for both CPI and GDP deflator inflation rates in terms of root mean squared prediction error. Also, the common finding of autonomous inflation volatility breaks is rejected by our approach: breaks in the conditional mean of inflation drive structural breaks in U.S. inflation measures.
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