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Analyzing the Full BINMA Time Series Process Using a Robust GQL Approach

Author

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  • Khan Naushad Mamode

    (Department of Economics and Statistics, University of Mauritius, Réduit, Mauritius)

  • Sunecher Yuvraj

    (University of Technology, La Tour Koenig, Port Louis, Mauritius)

  • Jowaheer Vandna

    (Department of Mathematics, Faculty of Science, University of Mauritius, Moka, Mauritius)

Abstract

We investigate a new bivariate-integer valued moving average time series process where the innovation series follow the bivariate Poisson assumption under stationary moments and constant cross-correlations. Furthermore, due to the complication involved in specifying the joint likelihood function, this paper considers a robust generalized quasi-likelihood approach to estimate the mean, serial and dependence parameters. Unlike previous estimation techniques such as the Generalized Least Squares, this estimation approach here involves a two-step Newton-Raphson iterative procedure where in the first step, the serial and cross correlations are estimated while in the second step, these dependence estimates are used to compute iteratively the vector of regression coefficients. The consistency of the estimates under this approach is checked through several simulation experiments under different combinations of low and high serial and cross-correlations.

Suggested Citation

  • Khan Naushad Mamode & Sunecher Yuvraj & Jowaheer Vandna, 2017. "Analyzing the Full BINMA Time Series Process Using a Robust GQL Approach," Journal of Time Series Econometrics, De Gruyter, vol. 9(2), pages 1-12, July.
    • Handle: RePEc:bpj:jtsmet:v:9:y:2017:i:2:p:12:n:1
    DOI: 10.1515/jtse-2015-0019
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    References listed on IDEAS

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    1. Kurt Brannas & A. M. M. Shahiduzzaman Quoreshi, 2010. "Integer-valued moving average modelling of the number of transactions in stocks," Applied Financial Economics, Taylor & Francis Journals, vol. 20(18), pages 1429-1440.
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    4. Quoreshi, A.M.M. Shahiduzzaman, 2008. "A vector integer-valued moving average model for high frequency financial count data," Economics Letters, Elsevier, vol. 101(3), pages 258-261, December.
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