Forecasting overdispersed INAR(1) count time series with negative binomial marginal

DOI10.1080/03610918.2021.1908559OpenAlexW3152949804MaRDI QIDQ6172610

Unnamed Author, T. V. Ramanathan, Akanksha S. Kashikar

Publication date: 20 July 2023

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2021.1908559

zbMATH Keywords

negative binomial distributiongeometric distributionnegative binomial thinningcoherent forecastinginteger autoregressive models

Mathematics Subject Classification ID

Graph representations (geometric and intersection representations, etc.) (05C62)

Cites Work

• Making and Evaluating Point Forecasts
• Autoregressive conditional negative binomial model applied to over-dispersed time series of counts
• Coherent forecasting for over-dispersed time series of count data
• A new geometric first-order integer-valued autoregressive (NGINAR(1)) process
• Discrete analogues of self-decomposability and stability
• Coherent forecasting for stationary time series of discrete data
• Poisson-Lindley INAR(1) model with applications
• On the performance of information criteria for model identification of count time series
• Estimation in an Integer-Valued Autoregressive Process with Negative Binomial Marginals (NBINAR(1))
• Modeling seasonality in space-time infectious disease surveillance data
• Autoregressive moving-average processes with negative-binomial and geometric marginal distributions
• FIRST-ORDER INTEGER-VALUED AUTOREGRESSIVE (INAR(1)) PROCESS
• Convolution-closed models for count time series with applications
• Some estimation and forecasting procedures in Possion-Lindley INAR(1) process
• Integer autoregressive models with structural breaks
• Probabilistic Forecasts, Calibration and Sharpness
• Predictive Model Assessment for Count Data

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