A GQL estimation approach for analysing non-stationary over-dispersed BINAR(1) time series |
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| Authors: | Yuvraj Sunecher Naushad Mamode Khan Vandna Jowaheer |
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| Institution: | 1. Department of Accounting and Finance, University of Technology, Reduit, Mauritiusyuvisun@yahoo.com;3. Department of Economics and Statistics, University of Mauritius, Reduit, Mauritius;4. Department of Mathematics, University of Mauritius, Reduit, Mauritius |
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| Abstract: | This paper proposes a generalized quasi-likelihood (GQL) function for estimating the vector of regression and over-dispersion effects for the respective series in the bivariate integer-valued autoregressive process of order 1 (BINAR(1)) with Negative Binomial (NB) marginals. The auto-covariance function in the proposed GQL is computed using some ‘robust’ working structures. As for the BINAR(1) process, the inter-relation between the series is induced mainly by the correlated NB innovations that are subject to different levels of over-dispersion. The performance of the GQL approach is tested via some Monte-Carlo simulations under different combination of over-dispersion together with low and high serial- and cross-correlation parameters. The model is also applied to analyse a real-life series of day and night accidents in Mauritius. |
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| Keywords: | Non-stationary BINAR(1) GQL over-dispersion negative binomial |
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