Analytic on long-run equilibrium between thailand’s economy and business tourism (Mice) industry using Bayesian inference

Abstract

This paper is proposed to study on the sections of computational econometric estimations of Thailand’s Business Tourism (MICE) sectors. The objective is to examine the relationship among GDP, demands and revenues of Business Tourism (MICE) industry in Thailand during the period 2010–2016, based on Bayesian Analysis. Bayesian Analysis is applied to estimated Business Tourism (MICE) parameters, combining with Markov Chain Monte Carlo (MCMC) simulations. Stationary and correlative trends of variable sets were checked by employing Bayesian Augmented Dickey-Fuller (ADF) unit-root test and Bayesian Autoregressive Distributed Lag (ARDL) model respectively. Moreover, dependent structure was scrutinized by using canonical (C-) vine Copula method. Empirically, the results imply that revenues contribute most to long-run as well as short-run GDP growth. However, in the structure of the Business Tourism (MICE) industry, the number of tourists is also a significant variable.