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Likelihood Inference for Exponential-Trawl Processes

In: The Fascination of Probability, Statistics and their Applications

Author

Listed:
  • Neil Shephard

    (Harvard University, Department of Economics)

  • Justin J. Yang

    (Harvard University, Department of Statistics)

Abstract

Integer-valued trawl processes are a class of serially correlated, stationary and infinitely divisible processes that Ole E. Barndorff-Nielsen has been working on in recent years. In this chapter, we provide the first analysis of likelihood inference for trawl processes by focusing on the so-called exponential-trawl process, which is also a continuous time hidden Markov process with countable state space. The core ideas include prediction decomposition, filtering and smoothing, complete-data analysis and EM algorithm. These can be easily scaled up to adapt to more general trawl processes but with increasing computation efforts.

Suggested Citation

  • Neil Shephard & Justin J. Yang, 2016. "Likelihood Inference for Exponential-Trawl Processes," Springer Books, in: Mark Podolskij & Robert Stelzer & Steen Thorbjørnsen & Almut E. D. Veraart (ed.), The Fascination of Probability, Statistics and their Applications, pages 251-281, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-25826-3_12
    DOI: 10.1007/978-3-319-25826-3_12
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