A note on filtering for long memory processes

  • Authors:

  • A. Thavaneswaran;C. C. Heyde

  • Affiliations:

  • Department of Statistics The University of Manitoba Winnipeg, Manitoba, Canada R3T 2N2;Columbia University and Australian National University Canberra, ACT 0200, Australia

  • Venue:
  • Mathematical and Computer Modelling: An International Journal
  • Year:
  • 2001

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Abstract

This paper illustrates the use of quasilikelihood methods of inference for a class of possibly long-memory processes such as H-sssi (self-similar stationary increments) processes and long-range dependent sequences. In particular, they can be used in a general derivation without assuming normality of the process; this extends the result of Gripenberg and Norros [1]. Recursive filtering for models with linear intensity is also discussed in some detail.