A GQL estimation approach for analysing non-stationary over-dispersed BINAR(1) time series. Issue 10 (3rd July 2017)

• Record Type: Journal Article
• Authors: Sunecher, Yuvraj; Khan, Naushad Mamode; Jowaheer, Vandna
• Is Part Of: Journal of statistical computation and simulation. Volume 87:Issue 10(2017)
• Page Start: 1911
• Page End: 1924
• Publication Date: 2017-07-03
• Subjects: Non-stationary -- BINAR(1) -- GQL -- over-dispersion -- negative binomial; 65C60 -- 62J12 -- 62H12 -- 62J20 -- 62J10; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2017.1296152
• Languages: English
• ISSNs: 0094-9655

Abstract

ABSTRACT: This paper proposes a generalized quasi-likelihood (GQL) function for estimating the vector of regression and over-dispersion effects for the respective series in the bivariate integer-valued autoregressive process of order 1 (BINAR(1)) with Negative Binomial (NB) marginals. The auto-covariance function in the proposed GQL is computed using some 'robust' working structures. As for the BINAR(1) process, the inter-relation between the series is induced mainly by the correlated NB innovations that are subject to different levels of over-dispersion. The performance of the GQL approach is tested via some Monte-Carlo simulations under different combination of over-dispersion together with low and high serial- and cross-correlation parameters. The model is also applied to analyse a real-life series of day and night accidents in Mauritius.