Optimal univariate inflation forecasting with symmetric stable shocks

Prasad V. Bidarkota () and J. Huston McCulloch
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J. Huston McCulloch: Department of Economics, The Ohio State University, 410 Arps Hall, 1945 North High Street, Columbus, OH 43210, USA, Postal: Department of Economics, The Ohio State University, 410 Arps Hall, 1945 North High Street, Columbus, OH 43210, USA

Journal of Applied Econometrics, 1998, vol. 13, issue 6, pages 659-670

Abstract: 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.

Date: 1998
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Working Paper: Optimal Univariate Inflation Forecasting with Symmetric Stable Shocks
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