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...

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Bibliographic Details
Published in:Journal of applied econometrics (Chichester, England) England), 1998-11, Vol.13 (6), p.659-670
Main Authors: Bidarkota, Prasad V., McCulloch, J. Huston
Format: Article
Language:English
Subjects:
Algorithms
Analytical forecasting
Econometrics
Economic models
Economic shock
Forecasting models
Forecasts
Inflation
Inflation expectations
Inflation rates
Inflation shocks
Kalman filters
Macroeconomic modeling
Mathematical economics
Modeling
Non Gaussianity
Optimization
Spatial dimension
Standard error
Studies
Uncertainty
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Summary: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.
ISSN:0883-7252
1099-1255
DOI:10.1002/(SICI)1099-1255(199811/12)13:6<659::AID-JAE481>3.0.CO;2-Q