INAR(1) Processes with Inflated-parameter Generalized Power Series Innovations-Reference-Cited by-同舟云学术

INAR(1) Processes with Inflated-parameter Generalized Power Series Innovations

Published:2020-05-19 Issue:2 Volume:12 Page:
ISSN:1941-1928
Container-title:Journal of Time Series Econometrics
language:en
Short-container-title:

Author:

Lívio Tito¹,Bourguignon Marcelo¹^ORCID,Nascimento Fernando²

Affiliation:

1. Departamento de Estatística , Universidade Federal do Rio Grande do Norte , Lagoa Nova , Natal , RN , Brazil

2. Departamento de Estatística , Universidade Federal do Piauí , Teresina , PI , Brazil

Abstract

Abstract In this paper, new models are studied by proposing the family of generalized power series distributions with inflated parameter (IGPSD) for the innovation process of the INAR(1) model. The main properties of the process were established, such as mean, variance, autocorrelation and transition probability. The methods of estimation by Yule–Walker and the conditional maximum likelihood were used to estimate the parameters of the models. Two particular cases of the INAR ( 1 )

$\left(1\right)$

model with IGPSD innovation were studied, named IPoINAR ( 1 )

$\left(1\right)$

and IGeoINAR ( 1 )

$\left(1\right)$

. Finally, in the real data example, a good performance of the proposed new models was observed.

Publisher

Walter de Gruyter GmbH

Subject

Economics and Econometrics

Reference20 articles.

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3. Bourguignon, M., and K. L. P. Vasconcellos. 2015. “First Order Non-negative Integer Valued Autoregressive Processes with Power Series Innovations.” Brazilian Journal of Probability and Statistics 29: 71–93. https://doi.org/10.1214/13-BJPS229.

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