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CONWAY-MAXWELL-POISSON AUTOREGRESSIVE MOVING AVERAGE MODEL FOR EQUIDISPERSED, UNDERDISPERSED, AND OVERDISPERSED COUNT DATA

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Autor(es):
Melo, Moizes [1] ; Alencar, Airlane [1]
Número total de Autores: 2
Afiliação do(s) autor(es):
[1] Univ Sao Paulo, Dept Stat, Sao Paulo - Brazil
Número total de Afiliações: 1
Tipo de documento: Artigo Científico
Fonte: JOURNAL OF TIME SERIES ANALYSIS; JUL 2020.
Citações Web of Science: 0
Resumo

In this work, we propose a dynamic regression model based on the Conway \& x16e;Maxwell-Poisson (CMP) distribution with time-varying conditional mean depending on covariates and lagged observations. This new class of Conway \& x16e;Maxwell-Poisson autoregressive moving average (CMP-ARMA) models is suitable for the analysis of time series of counts. The CMP distribution is a two-parameter generalization of the Poisson distribution that allows the modeling of underdispersed, equidispersed, and overdispersed data. Our main contribution is to combine this dispersion flexibility with the inclusion of lagged terms to model the conditional mean response, inducing an autocorrelation structure, usually relevant in time series. We present the conditional maximum likelihood estimation, hypothesis testing inference, diagnostic analysis, and forecasting along with their asymptotic properties. In particular, we provide closed-form expressions for the conditional score vector and conditional Fisher information matrix. We conduct a Monte Carlo experiment to evaluate the performance of the estimators in finite sample sizes. Finally, we illustrate the usefulness of the proposed model by exploring two empirical applications. (AU)

Processo FAPESP: 18/04654-9 - Séries temporais, ondaletas e dados de alta dimensão
Beneficiário:Pedro Alberto Morettin
Linha de fomento: Auxílio à Pesquisa - Temático