Inferential methods for an unconstrained nonstationary BINMA time series process with Poisson innovations

This article proposes an unconstrained nonstationary BINMA(1) time-series process with Poisson innovations under time-dependent moments where the cross-correlation structure is formed firstly by the jointly distributed innovations and second by relating the current variate observations with the prev...

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Bibliographic Details
Published inJournal of statistical theory and practice Vol. 11; no. 1; pp. 76 - 106
Main Authors Mamode Khan, N., Sunecher, Y., Jowaheer, V.
Format Journal Article
LanguageEnglish
Published Cham Taylor & Francis 02.01.2017
Springer International Publishing
Subjects
Bivariate
moving average
Probability Theory and Stochastic Processes
quasi-likelihood
Statistical Theory and Methods
Statistics
time series
Bivariate
time series
moving average
65C60
62J20
62H12
62J10
quasi-likelihood
62J12
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Summary:This article proposes an unconstrained nonstationary BINMA(1) time-series process with Poisson innovations under time-dependent moments where the cross-correlation structure is formed firstly by the jointly distributed innovations and second by relating the current variate observations with the previous lagged innovation of the other series and vice versa. For this new BINMA(1) time series model, feasible generalized least squares (FGLS), generalized method of moments (GMM), and generalized quasi-likelihood (GQL) estimating equations are developed. A simulation process is set up to generate BINMA(1) time-series data under the unconstrained cross-correlation structure. The purpose here is to assess the performance of the different estimation techniques proposed. The article also analyzes real-life monthly day and night accidents data in Mauritius under this model.
ISSN:1559-8608
1559-8616
DOI:10.1080/15598608.2016.1258600