Abstract
ABSTRACTThis 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.
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