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Estimation for a class of generalized state-space time series models

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  • Fukasawa, T.
  • Basawa, I. V.

Abstract

State-space models with exponential and conjugate exponential family densities are introduced. Examples include Poisson-Gamma, Binomial-Beta, Gamma-Gamma and Normal-Normal processes. Maximum likelihood and quasilikelihood estimators and their properties are discussed. Results from a simulation study for the Poisson-Gamma model are reported.

Suggested Citation

  • Fukasawa, T. & Basawa, I. V., 2002. "Estimation for a class of generalized state-space time series models," Statistics & Probability Letters, Elsevier, vol. 60(4), pages 459-473, December.
    • Handle: RePEc:eee:stapro:v:60:y:2002:i:4:p:459-473
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    References listed on IDEAS

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    1. Tanizaki, Hisashi, 1993. "Kalman Filter Model with Qualitative Dependent Variables," The Review of Economics and Statistics, MIT Press, vol. 75(4), pages 747-752, November.
    2. Gary K. Grunwald & Kais Hamza & Rob J. Hyndman, 1997. "Some Properties and Generalizations of Non‐negative Bayesian Time Series Models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 59(3), pages 615-626.
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    Cited by:

    1. Zheng, Haitao & Basawa, Ishwar V., 2008. "First-order observation-driven integer-valued autoregressive processes," Statistics & Probability Letters, Elsevier, vol. 78(1), pages 1-9, January.
    2. Chen Xi & Wang Lihong, 2013. "Conditional L1 estimation for random coefficient integer-valued autoregressive processes," Statistics & Risk Modeling, De Gruyter, vol. 30(3), pages 221-235, August.
    3. Kai Yang & Yao Kang & Dehui Wang & Han Li & Yajing Diao, 2019. "Modeling overdispersed or underdispersed count data with generalized Poisson integer-valued autoregressive processes," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 82(7), pages 863-889, October.

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