A simulation smoother for long memory time series with correlated and heteroskedastic additive noise

The purpose of this paper is to extend the work of So ( 1999 ) by accommodating heteroskedasticity in long memory processes and correlation in disturbances. We propose an alternative representation of long memory processes from which we develop filtering equations, prediction densities and a simulat...

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Bibliographic Details
Published inCommunications in statistics. Simulation and computation Vol. 50; no. 2; pp. 388 - 399
Main Authors Asai, Manabu, So, Mike K. P.
Format Journal Article
LanguageEnglish
Published Philadelphia Taylor & Francis 01.02.2021
Taylor & Francis Ltd
Subjects
Correlation
Filtering
Kalman filter
Kalman filters
Long Memory
Simulation
Simulation Smoother
State Space Models
Stochastic Volatility
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Summary:The purpose of this paper is to extend the work of So ( 1999 ) by accommodating heteroskedasticity in long memory processes and correlation in disturbances. We propose an alternative representation of long memory processes from which we develop filtering equations, prediction densities and a simulation smoother for latent state variables as in the classical Kalman filter. We illustrate the simulation smoother introduced in this paper by estimating a class of long memory models with heteroskedastic correlated additive noises.
ISSN:0361-0918
1532-4141
DOI:10.1080/03610918.2018.1554120