Likelihood Inference for Exponential-Trawl Processes

Neil Shephard () and Justin J. Yang ()
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Neil Shephard: Harvard University, Department of Economics
Justin J. Yang: Harvard University, Department of Statistics

A chapter in The Fascination of Probability, Statistics and their Applications, 2016, pp 251-281 from Springer

Abstract: 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.

Keywords: Integer-valued process; Trawl process; Poisson random measure; Hidden Markov process; Maximum likelihood estimate; Filtering and smoothing; EM algorithm (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-25826-3_12

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DOI: 10.1007/978-3-319-25826-3_12

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