New Methods for Forecasting Inflation, Applied to the US

Abstract

Models for the twelve-month-ahead US rate of inflation, measured by the chain weighted consumer expenditure deflator, are estimated for 1974-99 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. Three key ingredients to the out-performance are: including equilibrium correction terms in relative prices; introducing non-linearities 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' (PLL) parameterisation. Forecast pooling or averaging also improves forecast performance.

Keywords: Error Correction Models, Evaluating Forecasts, Model Selection, Multivariate Time Series

JEL Classification: C22, C51, C52, C53, E31, E37, E52