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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

    as
    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. repec:aer:wpaper:227 is not listed on IDEAS
    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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