scholarly journals INARMA Modeling of Count Time Series

Stats ◽  
2019 ◽  
Vol 2 (2) ◽  
pp. 284-320 ◽  
Author(s):  
Christian H. Weiß ◽  
Martin H.-J. M. Feld ◽  
Naushad Mamode Khan ◽  
Yuvraj Sunecher
Keyword(s):  
Time Series ◽  
Maximum Likelihood ◽  
Model Selection ◽  
Maximum Likelihood Estimation ◽  
Moving Average ◽  
Likelihood Estimation ◽  
Real Data ◽  
Autoregressive Moving Average ◽  
Count Time Series ◽  
The Given

While most of the literature about INARMA models (integer-valued autoregressive moving-average) concentrates on the purely autoregressive INAR models, we consider INARMA models that also include a moving-average part. We study moment properties and show how to efficiently implement maximum likelihood estimation. We analyze the estimation performance and consider the topic of model selection. We also analyze the consequences of choosing an inadequate model for the given count process. Two real-data examples are presented for illustration.

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