Maximum likelihood estimation for an observation driven model for Poisson counts

DOI10.1007/s11009-005-1480-4zbMath1078.62091OpenAlexW2075928553MaRDI QIDQ812972

Sarah B. Streett, William T. M. Dunsmuir, Richard A. Davis

Publication date: 30 January 2006

Published in: Methodology and Computing in Applied Probability (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11009-005-1480-4

zbMATH Keywords

GLARMA modelsobservation-driven modelPoisson valued time series

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic distribution theory in statistics (62E20)

Related Items (12)

Useful models for time series of counts or simply wrong ones? ⋮ Score-driven dynamic patent count panel data models ⋮ On conditions in central limit theorems for martingale difference arrays ⋮ Count Time Series: A Methodological Review ⋮ On weak dependence conditions for Poisson autoregressions ⋮ Inference and testing for structural change in general Poisson autoregressive models ⋮ First‐order integer valued AR processes with zero inflated poisson innovations ⋮ Some recent progress in count time series ⋮ Variable selection in sparse GLARMA models ⋮ Independence, successive and conditional likelihood for time series of counts ⋮ Time series of count data: a review, empirical comparisons and data analysis ⋮ Comments on: Some recent theory for autoregressive count time series

Cites Work

• M-estimation for autoregression with infinite variance
• Observation-driven models for Poisson counts
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