Approximate Bayesian Estimation for Multivariate Count Time Series Models

2015 ◽  
pp. 155-167 ◽  
Author(s):  
Volodymyr Serhiyenko ◽  
Nalini Ravishanker ◽  
Rajkumar Venkatesan
Keyword(s):  
Time Series ◽  
Bayesian Estimation ◽  
Time Series Models ◽  
Count Time Series ◽  
Approximate Bayesian

Zero-inflated count time series models using Gaussian copula

2019 ◽  
Vol 38 (3) ◽  
pp. 342-357 ◽  
Author(s):  
Mohammed Alqawba ◽  
Norou Diawara ◽  
N. Rao Chaganty
Keyword(s):  
Time Series ◽  
Gaussian Copula ◽  
Time Series Models ◽  
Count Time Series

SUPERPOSITIONED STATIONARY COUNT TIME SERIES

2019 ◽  
pp. 1-19
Author(s):  
Yisu Jia ◽  
Robert Lund ◽  
James Livsey
Keyword(s):  
Time Series ◽  
Negative Binomial ◽  
Stationary Sequence ◽  
Time Series Models ◽  
Count Time Series ◽  
Marginal Distributions ◽  
Autocovariance Function ◽  
Basic Properties ◽  
Model Class ◽  
Discrete Uniform

Abstract This paper probabilistically explores a class of stationary count time series models built by superpositioning (or otherwise combining) independent copies of a binary stationary sequence of zeroes and ones. Superpositioning methods have proven useful in devising stationary count time series having prespecified marginal distributions. Here, basic properties of this model class are established and the idea is further developed. Specifically, stationary series with binomial, Poisson, negative binomial, discrete uniform, and multinomial marginal distributions are constructed; other marginal distributions are possible. Our primary goal is to derive the autocovariance function of the resulting series.

Moment structures of parameter-driven count time series models

2017 ◽  
Author(s):  
Nawwal Ahmad Bukhari ◽  
Koh You Beng ◽  
Ibrahim Mohamed
Keyword(s):  
Time Series ◽  
Time Series Models ◽  
Count Time Series
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