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 series. We develop a univariate state space model with symmetric stable shocks for this series. The non-Gaussian model is estimated by the Sorenson-Alspach filtering algorithm. Even after removing conditional heteroscedasticity, normality is rejected in favour of a stable distribution with exponent 1·83. Our model can be used for forecasting future inflation, and to simulate historical inflation forecasts conditional on the history of inflation. Relative to the Gaussian model, the stable model accounts for outliers and level shifts better, provides tighter estimates of trend inflation, and gives more realistic assessment of uncertainty during confusing episodes. © 1998 John Wiley & Sons, Ltd.
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|Contact details of provider:|| Postal: CEF97, Stanford University, Department of Economics, Stanford CA USA|
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