pplication of generalised linear regression GARMA in tourism area

" From a modelling perspective, our first contribution is to propose generalised linear regression GARMA(GLRGARMA) model and generalised linear regression SARMA (GLRSARMA) model with a innovativefunction of explanatory variables in order to extend GLGARMA to incorporate relevant information formodel fitting and forecast in tourism area. Besides, the generalised Poisson (GP) distribution is adoptedto accommodate over- equal- and under-dispersion for certain tourism data. Moreover, the performance ofGLRGARMA model and GLRSARMA model with their nested sub-models are compared and evaluatedusing several well-known selection criteria.Our second contribution is to investigate the behaviour of tourism data. The pattern of long memoryis examined. The analysis of Hurst exponent, ACF plot and periodogram plot shows that Gegenbauerlong memory features are presented in tourism data. Furthermore, the distinct characteristics betweenGegenbauer long memory and seasonality are demonstrated to reveal the that the GLRGARMA model ismore suitable for modelling tourism data.Our third contribution is to derive a Bayesian approach via the efficient and user-friendly Rstan packagein estimating our proposed models. For ML approach, the likelihood function is untractable becauseof involving very high dimensional integrals. Several monitors of convergence of posterior samples arediscussed, such as the number of effective sample and bRestimate. The criteria for modelling performanceare also derived.