Optimal univariate inflation forecasting with symmetric stable shocks
AbstractMonthly 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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Bibliographic InfoArticle provided by John Wiley & Sons, Ltd. in its journal Journal of Applied Econometrics.
Volume (Year): 13 (1998)
Issue (Month): 6 ()
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Web page: http://www.interscience.wiley.com/jpages/0883-7252/
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- Prasad V. Bidarkota & J. Huston McCulloch, . "Optimal Univariate Inflation Forecasting with Symmetric Stable Shocks," Computing in Economics and Finance 1997 116, Society for Computational Economics.
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- Prasad V. Bidarkota & Brice V. Dupoyet & J. Huston McCulloch, 2005. "Asset Pricing with Incomplete Information under Stable Shocks," Working Papers 0514, Florida International University, Department of Economics.
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- J. Huston McCulloch, 2005. "The Kalman Foundations of Adaptive Least Squares: Applications to Unemployment and Inflation," Computing in Economics and Finance 2005 239, Society for Computational Economics.
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