Estimating the parameters of a BINMA Poisson model for a non-stationary bivariate time series |
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Authors: | Yuvraj Sunecher Vandna Jowaheer |
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Affiliation: | 1. Department of Accounting and Finance, University of Technology, Mauritius, La Tour Koenig, Mauritius;2. Department of Mathematics, University of Mauritius, Reduit, Mauritius |
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Abstract: | This article proposes a novel non-stationary BINMA time series model by extending two INMA processes where their innovation series follow the bivariate Poisson under time-varying moment assumptions. This article also demonstrates, through simulation studies, the use and superiority of the generalized quasi-likelihood (GQL) approach to estimate the regression effects, which is computationally less complicated as compared to conditional maximum likelihood estimation (CMLE) and the feasible generalized least squares (FGLS). The serial and bivariate dependence correlations are estimated by a robust method of moments. |
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Keywords: | Bivariate Moving average Quasi-likelihood Time series |
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