Has the Volatility of U.S. Inflation Changed and How?
The local level model with stochastic volatility, recently proposed for U.S. by Stock and Watson (Why Has U.S. Inflation Become Harder to Forecast?, Journal of Money, Credit and Banking, Supplement to Vol. 39, No. 1, February 2007), provides a simple yet sufficently rich framework for characterizing the evolution of the main stylized facts concerning the U.S. inflation. The model decomposes inflation into a core component, evolving as a random walk, and a transitory component. The volatility of the disturbances driving both components is allowed to vary over time. The paper provides a full Bayesian analysis of this model and readdresses some of the main issues that were raised by the literature concerning the evolution of persistence and predictability and the extent and timing of the great moderation. The assessment of various nested models of inflation volatility and systematic model selection provide strong evidence in favor of a model with heteroscedastic disturbances in the core component, whereas the transitory component has time invariant size. The main evidence is that the great moderation is over, and that volatility, persistence and predictability of inflation underwent a turning point in the late 1990s. During the last decade volatility and persistence have been increasing and predictability has been going down.
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- Charles S. Bos & Neil Shephard, 2004.
"Inference for Adaptive Time Series Models: Stochastic Volatility and Conditionally Gaussian State Space form,"
Tinbergen Institute Discussion Papers
04-015/4, Tinbergen Institute.
- Charles Bos & Neil Shephard, 2006. "Inference for Adaptive Time Series Models: Stochastic Volatility and Conditionally Gaussian State Space Form," Econometric Reviews, Taylor & Francis Journals, vol. 25(2-3), pages 219-244.
- Charles S. Bos & Neil Shephard, 2004. "Inference for Adaptive Time Series Models: Stochastic Volatility and Conditionally Gaussian State Space Form," Economics Papers 2004-W02, Economics Group, Nuffield College, University of Oxford.
- Charles S. Bos & Siem Jan Koopman & Marius Ooms, 2007.
"Long memory modelling of inflation with stochastic variance and structural breaks,"
CREATES Research Papers
2007-44, Department of Economics and Business Economics, Aarhus University.
- C.S. Bos & S.J. Koopman & M. Ooms, 2007. "Long Memory Modelling of Inflation with Stochastic Variance and Structural Breaks," Tinbergen Institute Discussion Papers 07-099/4, Tinbergen Institute.
- Timothy Cogley & Giorgio E. Primiceri & Thomas J. Sargent, 2008.
"Inflation-Gap Persistence in the U.S,"
NBER Working Papers
13749, National Bureau of Economic Research, Inc.
- Pivetta, Frederic & Reis, Ricardo, 2007. "The persistence of inflation in the United States," Journal of Economic Dynamics and Control, Elsevier, vol. 31(4), pages 1326-1358, April.
- J. Durbin, 2002. "A simple and efficient simulation smoother for state space time series analysis," Biometrika, Biometrika Trust, vol. 89(3), pages 603-616, August.
- Chib S. & Jeliazkov I., 2001. "Marginal Likelihood From the Metropolis-Hastings Output," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 270-281, March.
- Sangjoon Kim & Neil Shephard & Siddhartha Chib, 1998. "Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models," Review of Economic Studies, Oxford University Press, vol. 65(3), pages 361-393.
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