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

Author

Listed:
  • Miroslav M. Ristić

    (University of Niš)

  • Yuvraj Sunecher

    (University of Technology Mauritius)

  • Naushad Mamode Khan

    (University of Mauritius)

  • Vandna Jowaheer

    (University of Mauritius)

Abstract

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.

Suggested Citation

  • Miroslav M. Ristić & Yuvraj Sunecher & Naushad Mamode Khan & Vandna Jowaheer, 2019. "A GQL-based inference in non-stationary BINMA(1) time series," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(3), pages 969-998, September.
    • Handle: RePEc:spr:testjl:v:28:y:2019:i:3:d:10.1007_s11749-018-0615-1
    DOI: 10.1007/s11749-018-0615-1
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    References listed on IDEAS

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    1. Wei Pan, 2001. "Akaike's Information Criterion in Generalized Estimating Equations," Biometrics, The International Biometric Society, vol. 57(1), pages 120-125, March.
    2. Schweer, Sebastian & Weiß, Christian H., 2014. "Compound Poisson INAR(1) processes: Stochastic properties and testing for overdispersion," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 267-284.
    3. S.K. Bundoo, 2011. "An Analysis of Stock Market Anomalies and Momentum Strategies on the Stock Exchange of Mauritius," Working Papers 227, African Economic Research Consortium, Research Department.
    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.
    5. Vandna Jowaheer, 2002. "Analysing longitudinal count data with overdispersion," Biometrika, Biometrika Trust, vol. 89(2), pages 389-399, June.
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    Cited by:

    1. Cláudia Santos & Isabel Pereira & Manuel G. Scotto, 2021. "On the theory of periodic multivariate INAR processes," Statistical Papers, Springer, vol. 62(3), pages 1291-1348, June.

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