Optimal univariate inflation forecasting with symmetric stable shocks

Monthly inflation in the United States indicates non-normality in the form of either occasional big shocks or marked changes in the level of the serie s. We develop a univariate state space model with symmetric stable shocks f or this series. The non-Gaussian model is estimated by the Sorenson-Alspach filtering algorithm. Even after removing conditional heteroscedasticity, n ormality is rejected in favour of a stable distribution with exponent 1.83. Our model can be used for forecasting future inflation, and to simulate hi storical inflation forecasts conditional on the history of inflation. Relat ive to the Gaussian model, the stable model accounts for outliers and level shifts better, provides tighter estimates of trend inflation, and gives mo re realistic assessment of uncertainty during confusing episodes. (C) 1998 John Wiley & Sons, Ltd.