A GQL-based inference in non-stationary BINMA(1) time series

• Miroslav M. Ristić ^[1] ; Yuvraj Sunecher ^[2] ; Naushad Mamode Khan ^[3] ; Vandna Jowaheer ^[3]
  1. [1] University of Nis

     University of Nis

     Serbia

     
  2. [2] University of Technology, Mauritius

     University of Technology, Mauritius

     Mauricio

     
  3. [3] University of Mauritius

     University of Mauritius

     Mauricio

     

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• Localización: Test: An Official Journal of the Spanish Society of Statistics and Operations Research, ISSN-e 1863-8260, ISSN 1133-0686, Vol. 28, Nº. 3, 2019, págs. 969-998
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• Resumen

  • This paper introduces a non-stationary bivariate integer-valued moving average of first-order (BINMA(1)) model with corresponding negative binomial innovations under different levels of over-dispersion that are pairwise unrelated. In the proposed BINMA(1), the interrelation between the series is induced by the relation of the current observation with the previous-lagged innovation of the other series, while the non-stationarity is captured through the time-variant covariate specification. Under such condition, the likelihood construction is cumbersome to formulate. Thus, a generalized quasi-likelihood equation based on an exact auto-covariance specification via multivariate thinning structures is proposed to estimate the regression, over-dispersion and dependence effects, and its performance and efficiency measures are compared with other common established techniques: generalized least squares and generalized method of moment based on simulated data from the proposed model under different scenarios of over-dispersion and serial coefficients. The model is further applied to analyze the intraday transactions of two major banks in Mauritius