New Methods for Forecasting Inflation, Applied to the US

Models for the 12-month-ahead US rate of inflation, measured by the chain-weighted consumer expenditure deflator, are estimated for 1974-98 and subsequent pseudo out-of-sample forecasting performance is examined. Alternative forecasting approaches for different information sets are compared with benchmark univariate autoregressive models, and substantial out-performance is demonstrated including against Stock and Watson's unobserved components-stochastic volatility model. Three key ingredients to the out-performance are: including equilibrium correction component terms in relative prices; introducing nonlinearities to proxy state-dependence in the inflation process and replacing the information criterion, commonly used in VARs to select lag length, with a parsimonious longer lags' parameterization. Forecast pooling or averaging also improves forecast performance.