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Research Article

Bivariate INAR(1) model under negative binomial innovations with non-homogeneous over-dispersed indices and application

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Pages 665-698 | Received 05 Nov 2022, Accepted 09 Oct 2023, Published online: 22 Oct 2023

References

  • Pedeli X, Karlis D. Bivariate INAR(1) models [technical report]. Athens University of Economics; Athens, Greece, 2009.
  • Pedeli X, Karlis D. A bivariate INAR(1) process with application. Stat Model An Int J. 2011;11:325–349. doi: 10.1177/1471082X1001100403
  • McKenzie E. Autoregressive moving-average processes with negative binomial and geometric marginal distributions. Adv Appl Probab. 1986;18:679–705. doi: 10.2307/1427183
  • Kocherlakota S, Kocherlakota K. Regression in the bivariate poisson distribution. Commun Stat Theory Methods. 2001;30(5):815–825. doi: 10.1081/STA-100002259
  • Karlis D, Pedeli X. Flexible bivariate INAR(1) processes using copulas. Commun Stat Theory Methods. 2013;42:723–740. doi: 10.1080/03610926.2012.754466
  • Marshall A, Olkin I. Multivariate distribution generated from mixtures of convolution and product families. Topics in Statistical Dependence, Block, Sampson y Sanits (Eds), Institute of Mathematical Statistics, 1990.
  • Pedeli X, Karlis D. Some properties of multivariate INAR(1) processes.. Comput Stat Data Anal. 2013a;67:213–225. doi: 10.1016/j.csda.2013.05.019
  • Cui Y, Zhu F. A bivariate integer-valued GARCH model allowing for negative cross-correlation. TEST. 2018;27:428–452. doi: 10.1007/s11749-017-0552-4
  • Ristic M, Nastic A, Jayakumar K, et al. A bivariate INAR(1) time series model with geometric marginals. Appl Math Lett. 2012;25(3):481–485. doi: 10.1016/j.aml.2011.09.040
  • Nastic A, Ristic M, Popovic P. Estimation in a bivariate integer-valued autoregressive process. Commun Stat Theory Methods. 2016b;45(19):5660–5678. doi: 10.1080/03610926.2014.948203
  • Popovic P, Ristic M, Nastic A. A geometric bivariate time series with different marginal parameters. Stat Papers. 2016;57:731–753. doi: 10.1007/s00362-015-0677-z
  • Pedeli X, Karlis D. On composite likelihood estimation of a multivariate INAR(1) model.. J Time Ser Anal. 2013c;34:206–220. doi: 10.1111/jtsa.12003
  • Nastic A, Laketa P, Ristic M. Random environment integer-valued autoregressive process. J Time Ser Anal. 2016a;37(2):267–287. doi: 10.1111/jtsa.v37.2
  • Mamode Khan N, Sunecher Y, Jowaheer V. Modelling a non-stationary BINAR(1) poisson process. J Stat Comput Simul. 2016b;86:3106–3126. doi: 10.1080/00949655.2016.1150482
  • Sunecher Y, Mamodekhan N, Jowaheer V. A GQL estimation approach for analysing non-stationary over-dispersed BINAR(1) time series. J Stat Comput Simul. 2017;87(10):1911–1924.
  • Sutradhar B, Das K. On the efficiency of regression estimators in generalised linear models for longitudinal data. Biometrika. 1999;86:459–65. doi: 10.1093/biomet/86.2.459
  • Mallick T, Sutradhar B. GQL versus conditional GQL inferences for non-stationary time series of counts with overdispersion. J Time Ser Anal. 2008;29(2):402–420. doi: 10.1111/j.1467-9892.2007.00570.x
  • Sutradhar B, Jowaheer V, Rao P. Remarks on asymptotic efficient estimation for regression effects in stationary and non-stationary models for panel count data. Braz J Probab Stat. 2014;28(2):241–254. doi: 10.1214/12-BJPS204
  • Sunecher Y, Mamodekhan N, Jowaheer V. Estimating the parameters of a BINMA Poisson model for a non-stationary bivariate time series. Commun Stat Simul Comput. 2016;46(9):6803–6827.
  • Franke J, Rao T. Multivariate first-order integer-valued autoregressions [technical report]. University of Kaiserslautern; Kaiserslautern, Germany, 1995.
  • Latour A. The multivariate GINAR(p) process. Adv Appl Probab. 1997;29:228–248. doi: 10.2307/1427868
  • Scotto M, Weiβ C, Silva M, et al. Bivariate binomial autoregressive models.. J Multivariate Time Ser. 2014;125:233–251.
  • Steutel F, Van Harn K. Discrete analogues of self-decomposability and stability. Ann Probab. 1979;7:3893–3899. doi: 10.1214/aop/1176994950
  • Jowaheer V, Sutradhar B. Analysing longitudinal count data with overdipsersion. Biometrika. 2002;89:389–399. doi: 10.1093/biomet/89.2.389
  • Steutel F, Van Harn K. Discrete operator self-decomposability and queing networks. Stoch Models. 1986;2:161–169. doi: 10.1080/15326348608807031
  • McKenzie E. Some ARMA models for dependent sequences of poisson counts. Adv Appl Probab. 1988;20:822–835. doi: 10.2307/1427362
  • Sutradhar B. An overview on regression models for discrete longitudinal responses. Stat Sci. 2003;18(3):377–393. doi: 10.1214/ss/1076102426
  • Wedderburn R. Quasi-likelihood functions, generalized linear models, and the Gauss–Newton method. Biometrika. 1974;61(3):439–47.
  • McCullagh P, Nelder J. Generalized linear models. 2nd ed. New York: Chapman and Hall; 1999. p. 26.
  • Sutradhar B. Best practice recommendation for forecasting counts [technical report]. Canada: Department of Mathematics and Statistics, Memorial University of Newfoundland; 2008.
  • Freeland R, McCabe B. Analysis of low count time series data by poisson autoregression. J Time Ser Anal. 2004;25(5):701–722. doi: 10.1111/jtsa.2004.25.issue-5
  • Freeland R, McCabe B. Forecasting discrete valued low count time series. Int J Forecast. 2004b;20:427–434. doi: 10.1016/S0169-2070(03)00014-1
  • Ristic M, Popovic B. A new bivariate binomial time series model. Markov Process Relat. 2019;25:301–328.
  • Mamode Khan N, Cekim H, Ozel G. The family of the bivariate integer-valued autoregressive process (BINAR(1)) with Poisson-Lindley (PL) innovations. J Stat Comput Simul. 2020;90(4):624–637. doi: 10.1080/00949655.2019.1694929

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