Analysis of seasonal tourism demand to economic growth in Thailand: Bayesian approach

Abstract

Abstract This study investigates the dynamic empirical link between tourism demand (tourist arrivals, tourism revenues and tourism expenditures) and economic growth in the case of Thailand using a quarterly time-series data set from 2013q1 to 2018q4. The combination of Bayesian approach and Markov Chain Monte Carlo (MCMC) simulations can be applied and employed to estimate the parameters of tourism demand and economic growth. Stationary and correlative trends of variables datasets were examined by using Bayesian ADF unit-root testing (BADF), Bayesian seasonal unit-root testing (BHEGY) and Bayesian Auto Regressive Distributed Lag (BARDL) model respectively. BADF is applied in order to probe the stationary of the time-series data set. Moreover, BHEGY is utilized in order to examine the seasonally of the time-series data set. Furthermore, BARDL technique is used and implemented in order to analyse the long-run and short-run relationship between tourism demand and economic growth. Our empirical findings provide important policy implications for further study on Thailand tourism.