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New Approaches to the Analysis of Count Time Series

Title
New Approaches to the Analysis of Count Time Series
Funding
ARC | Discovery Projects
Contract (GA) number
DP0450257
Start Date
2004/01/01
End Date
2006/12/31
Open Access mandate
no
Organizations
-
More information
http://purl.org/au-research/grants/arc/DP0450257

 

  • An Assessment of Alternative State Space Models for Count Time Series

    Ralph D. Snyder; Gael M. Martin; Phillip Gould; Paul D. Feigin (2007)
    Projects: ARC | New Approaches to the Analysis of Count Time Series (DP0450257)
    This paper compares two alternative models for autocorrelated count time series. The first model can be viewed as a 'single source of error' discrete state space model, in which a time-varying parameter is specified as a function of lagged counts, with no additional source of error introduced. The second model is the more conventional 'dual source of error' discrete state space model, in which the time-varying parameter is driven by a random autocorrelated process. Using the nomenclature of t...

    Testing for Dependence in Non-Gaussian Time Series Data

    Keith Freeland; Brendan McCabe; Gael Martin (2004)
    Projects: ARC | New Approaches to the Analysis of Count Time Series (DP0450257)
    This paper provides a general methodology for testing for dependence in time series data, with particular emphasis given to non-Gaussian data. A dynamic model is postulated for a continuous latent variable and the dynamic structure transferred to the non-Gaussian, possibly discrete, observations. Locally most powerful tests for various forms of dependence are derived, based on an approximate likelihood function. Invariance to the distribution adopted for the data, conditional on the latent pr...

    Parameterisation and Efficient MCMC Estimation of Non-Gaussian State Space Models

    Chris M Strickland; Gael Martin; Catherine S Forbes (2006)
    Projects: ARC | New Approaches to the Analysis of Count Time Series (DP0450257)
    The impact of parameterisation on the simulation efficiency of Bayesian Markov chain Monte Carlo (MCMC) algorithms for two non-Gaussian state space models is examined. Specifically, focus is given to particular forms of the stochastic conditional duration (SCD) model and the stochastic volatility (SV) model, with four alternative parameterisations of each model considered. A controlled experiment using simulated data reveals that relationships exist between the simulation efficiency of the MC...
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