Time Series and Model Selection

2008 ◽  
Vol 37 (4) ◽  
pp. 766-771 ◽  
Author(s):  
A. E. Clark ◽  
C. G. Troskie
Keyword(s):  
2018 ◽  
Vol 6 (6) ◽  
pp. 1101-1108
Author(s):  
P Mariyappan ◽  
◽  
P Arumugam ◽  

2021 ◽  
pp. 1471082X2110347
Author(s):  
Panagiota Tsamtsakiri ◽  
Dimitris Karlis

There is an increasing interest in models for discrete valued time series. Among them, the integer autoregressive conditional heteroscedastic (INGARCH) is a model that has found several applications. In the present article, we study the problem of model selection for this family of models. Namely we consider that an observation conditional on the past follows a Poisson distribution where its mean depends on its past mean values and on past observations. We consider both linear and log-linear models. Our purpose is to select the most appropriate order of such models, using a trans-dimensional Bayesian approach that allows jumps between competing models. A small simulation experiment supports the usage of the method. We apply the methodology to real datasets to illustrate the potential of the approach.


Author(s):  
S. O. Ongbali ◽  
A. C. Igboanugo ◽  
S. A. Afolalu ◽  
M. O. Udo ◽  
I. P. Okokpujie

2009 ◽  
Vol 2 (1) ◽  
pp. 68
Author(s):  
Eugene Novikov ◽  
Emmanuel Barillot

2010 ◽  
Vol 21 (3) ◽  
pp. 595-610 ◽  
Author(s):  
J. P. Florido ◽  
H. Pomares ◽  
I. Rojas ◽  
J. M. Urquiza ◽  
M. A. Lopez-Gordo

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