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Long memory modelling of inflation with stochastic variance and structural breaks

Listed author(s):
  • Charles S. Bos
  • Siem Jan Koopman
  • Marius Ooms

    ()

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

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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File URL: ftp://ftp.econ.au.dk/creates/rp/07/rp07_44.pdf
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Paper provided by Department of Economics and Business Economics, Aarhus University in its series CREATES Research Papers with number 2007-44.

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Length: 27
Date of creation: 21 Dec 2007
Handle: RePEc:aah:create:2007-44
Contact details of provider: Web page: http://www.econ.au.dk/afn/

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