Persistence, Excess Volatility, and Volatility Clusters in Inflation (ICPSR 1251)

Version Date: Mar 8, 2002 View help for published

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Michael T. Owyang, Federal Reserve Bank of St. Louis

https://doi.org/10.3886/ICPSR01251.v1

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This paper presents a single, integrated model to explain the persistence and volatility characteristics of the United States inflation time series. Policymaker learning about a Markov-switching natural rate of unemployment in a neoclassical Phillips curve model with time-varying preferences produces inflation persistence, volatility clustering, and mean/variance correlation. The interaction between the policymaker's preferences and the Phillips curve generates the first and last results. Policymaker learning produces clusters of volatility as the monetary authority resets the learning algorithm whenever a shock to the Phillips curve occurs. Simulations using parameters estimated via Gibbs sampling confirms the theory.

Owyang, Michael T. Persistence, Excess Volatility, and Volatility Clusters in Inflation. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2002-03-08. https://doi.org/10.3886/ICPSR01251.v1

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2002-03-08

2018-02-15 The citation of this study may have changed due to the new version control system that has been implemented. The previous citation was:

  • Owyang, Michael T. Persistence, Excess Volatility, and Volatility Clusters in Inflation. ICPSR01251-v1. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2002-03-08. http://doi.org/10.3886/ICPSR01251.v1

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