An Analysis of the Effects of Tourism Demand, Yield Curve, and Stock Returns on Economic Growth of Thailand: A Comparison Between the Bayesian DCC-GARCH and Bayesian Change-Point Methods

Abstract

In this paper, the Bayesian DCC-GARCH model is applied to find the relationship among economic growth, tourism demand, yield curve, and stock return in Thailand from 2013 to 2020, based on 30 quarterly observations. Only the time-varying correlation between real GDP and US tourist arrivals is positive and the volatility significantly increased around quarter 25 to quarter 28. The Bayesian change-point method is used to detect the sudden changes in the dependent variable time-series. There is one change point at quarter 28. Both methodologies provide similar evidence in that there exists a time-varying correlation, and a change-point in quarter 28 (July–October 2020) which coincides with the reopening time of Thailand after the Covid-19 lockdown. Even though two approaches are different, similar results are obtained in the relationship of various explanatory variables with economic growth. return from stocks, yield curve, and tourism demand are found to positively affect economic growth. Moreover, it is advised that the two methods could be used in future study to describe the real-world situation with different characteristics of the data set to provide more evidence.