Note on integer-valued bilinear time series models

DOI10.1016/j.spl.2007.10.008zbMath1416.62484OpenAlexW3122712859MaRDI QIDQ928969

Ramon van den Akker, Feike C. Drost, Bas J. M. Werker

Publication date: 11 June 2008

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.spl.2007.10.008

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10)

Related Items (15)

Generalized RCINAR(1) Process with Signed Thinning Operator ⋮ Integer-Valued Self-Exciting Threshold Autoregressive Processes ⋮ A special integer-valued bilinear time series model with applications ⋮ The asymptotic structure of nearly unstable non-negative integer-valued AR(1) models ⋮ Integer-valued bilinear model with dependent counting series ⋮ Some recent theory for autoregressive count time series ⋮ A bivariate integer-valued bilinear autoregressive model with random coefficients ⋮ A non-linear random environment \(\mathrm{INAR}(1)\) model ⋮ Local asymptotic normality and efficient estimation for INAR(p) models ⋮ On weak dependence conditions: the case of discrete valued processes ⋮ Generalized RCINAR(p) Process with Signed Thinning Operator ⋮ Thinning-based models in the analysis of integer-valued time series: a review ⋮ Inference for INAR\((p)\) processes with signed generalized power series thinning operator ⋮ Infinitely Divisible Distributions in Integer‐Valued Garch Models ⋮ An integer-valued bilinear time series model via two random operators

Uses Software

• Fahrmeir

Cites Work

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• Markov chains and stochastic stability
• THE INTEGER-VALUED AUTOREGRESSIVE (INAR(p)) MODEL
• Binomial autoregressive moving average models
• An integer-valued pth-order autoregressive structure (INAR(p)) process
• Existence and Stochastic Structure of a Non-negative Integer-valued Autoregressive Process
• FIRST-ORDER INTEGER-VALUED AUTOREGRESSIVE (INAR(1)) PROCESS
• A simple integer-valued bilinear time series model
• Splitting a Single State of a Stationary Process into Markovian States
• Multivariate statistical modelling based on generalized linear models.

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