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Long Memory Modelling of Inflation with Stochastic Variance and Structural Breaks

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  • C.S. Bos

    ()
    (VU University Amsterdam)

  • S.J. Koopman

    ()
    (VU University Amsterdam)

  • M. Ooms

    ()
    (VU University Amsterdam)

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 Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 07-099/4.

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Date of creation: 18 Dec 2007
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Handle: RePEc:dgr:uvatin:20070099

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Web page: http://www.tinbergen.nl

Related research

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

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Cited by:
  1. Josu Arteche, 2012. "Standard and seasonal long memory in volatility: an application to Spanish inflation," Empirical Economics, Springer, Springer, vol. 42(3), pages 693-712, June.
  2. Grassi Stefano & Proietti Tommaso, 2010. "Has the Volatility of U.S. Inflation Changed and How?," Journal of Time Series Econometrics, De Gruyter, De Gruyter, vol. 2(1), pages 1-22, September.

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