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

Miroslav M. Ristić (), Yuvraj Sunecher (), Naushad Mamode Khan () and Vandna Jowaheer ()
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Miroslav M. Ristić: University of Niš
Yuvraj Sunecher: University of Technology Mauritius
Naushad Mamode Khan: University of Mauritius
Vandna Jowaheer: University of Mauritius

TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2019, vol. 28, issue 3, No 19, 969-998

Abstract: 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.

Keywords: BINMA(1); GQL; Negative binomial; Stock transaction; Over-dispersion; 65C60; 62J12; 62H12; 62J20; 62J10 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11749-018-0615-1

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