Autocovariance and autocorrelation structures of the generalised autoregressive moving average (GARMA(1,3;δ,1)) model
DOI:
https://doi.org/10.11113/mjfas.v13n2.571Keywords:
generalised ARMA model, GAR, GMA, autocovariance, autocorrelationAbstract
Generalized ARMA (GARMA) model is a new class of model that has been introduced to reveal some unknown features of certain time series data. The objective of this paper is to derive the autocovariance and autocorrelation structure of GARMA(1,3;δ,1) model in order to study the behaviour of the model. It is shown that the results of this model can be reduced to the autocovariance and autocorrelation of the standard ARMA model as well as a special case. Numerical examples are used to illustrate the behaviour of the autocovariance and autocorrelation at different δ values to show the various structures that the model can representReferences
Brockwell, P.J. and Davis, R. A. 2002. Introduction to time series and forecasting. New York: Springer-Verlag. p. 49–50.
Gershenfeld, N. 1999. The nature of mathematical modeling. New York: Cambridge University Press. p. 205–208.
Peiris, M. S. 2003. Improving the quality of forecasting using Generalized AR models: An application to statistical quality control, Statistical Methods. 5(2), 156-171.
Peiris, M. S., Allen, D. and Thavaneswaran, A. 2004. An introduction to Generalized Moving Average models and applications. Journal of Applied Statistical Science. 13(3), 251-267.
Pillai, T. R. and Shitan, M. 2014. Some properties of the Generalised Autoregressive Moving Average "GARMA" (1,2;δ,1) model. Communications in Statistics - Theory and Methods. doi: 10.1080/03610926.2013.851240. (In press).
Pillai, T. R., Shitan, M. and Peiris, M. S. 2009. Time series properties of the class of first order autoregressive processes with generalized moving average errors. Journal of Statistics: Advances in theory and applications. 2(1), 71-92.
Pillai, T. R., Shitan, M. and Peiris, M. S. 2012. Some properties of the Generalized Autoregressive Moving Average ("GARMA" (1,1;δ_1,δ_2 )) model. Communications in Statistics - Theory and Methods. 41(4), 699-716.
Shitan, M. and Peiris, M. S. 2011. Time series properties of the class of generalized first-order autoregressive processes with moving average errors. Communications in Statistics - Theory and Methods. 40(13), 2259-2275.