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 innovative
function of explanatory variables in order to extend GLGARMA to incorporate relevant information for
model fitting and forecast in tourism area. Besides, the generalised Poisson (GP) distribution is adopted
to accommodate over- equal- and under-dispersion for certain tourism data. Moreover, the performance of
GLRGARMA model and GLRSARMA model with their nested sub-models are compared and evaluated
using several well-known selection criteria.
Our second contribution is to investigate the behaviour of tourism data. The pattern of long memory
is examined. The analysis of Hurst exponent, ACF plot and periodogram plot shows that Gegenbauer
long memory features are presented in tourism data. Furthermore, the distinct characteristics between
Gegenbauer long memory and seasonality are demonstrated to reveal the that the GLRGARMA model is
more suitable for modelling tourism data.
Our third contribution is to derive a Bayesian approach via the efficient and user-friendly Rstan package
in estimating our proposed models. For ML approach, the likelihood function is untractable because
of involving very high dimensional integrals. Several monitors of convergence of posterior samples are
discussed, such as the number of effective sample and bR
estimate. The criteria for modelling performance
are also derived.
闫弘轩.(2021).Application of generalised linear regression GARMA in tourism area.[ChinaXiv:202102.00001] (Click&Copy)