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Long memory modelling of inflation with stochastic variance and structural breaks

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Author Info

  • Charles S. Bos
  • Siem Jan Koopman
  • Marius Ooms

    ()
    (School of Economics and Management, University of Aarhus, Denmark and CREATES)

Abstract

We investigate changes in the time series characteristics of postwar U.S. inflation. In a model-based analysis the conditional mean of inflation is specified by a long memory autoregressive fractionally integrated moving average process and the conditional variance is modelled by a stochastic volatility process. We develop a Monte Carlo maximum likelihood method to obtain efficient estimates of the parameters using a monthly dataset of core inflation for which we consider different subsamples of varying size. Based on the new modelling framework and the associated estimation technique, we find remarkable changes in the variance, in the order of integration, in the short memory characteristics and in the volatility of volatility.

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Bibliographic Info

Paper provided by School of Economics and Management, University of Aarhus in its series CREATES Research Papers with number 2007-44.

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Length: 27
Date of creation: 21 Dec 2007
Date of revision:
Handle: RePEc:aah:create:2007-44

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Web page: http://www.econ.au.dk/afn/

Related research

Keywords: Time varying parameters; Importance sampling; Monte Carlo simulation; Stochastic Volatility; Fractional Integration;

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References

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Citations

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Cited by:
  1. Grassi, Stefano & Proietti, Tommaso, 2008. "Has the Volatility of U.S. Inflation Changed and How?," MPRA Paper 11453, University Library of Munich, Germany.
  2. Josu Arteche, 2012. "Standard and seasonal long memory in volatility: an application to Spanish inflation," Empirical Economics, Springer, vol. 42(3), pages 693-712, June.

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