Forecasting Tourist Arrivals in Greece and the Impact of Macroeconomic Shocks from the Countries of Tourists’ Origin

Abstract

This paper generates short-term forecasts on tourist arrivals in Greece and performs impulse response analysis to measure the impact of macroeconomic shocks from the origin country on future tourism demand. We find the ARIMA (1, 1, 1) model outperforms exponential smoothing models in forecasting the direction of one year out of sample forecasts. However, this does not translate into point forecasting accuracy. Impulse response analysis on the impact of unemployment and tourists' cost of living shocks shows that the source of downside risk to future tourism numbers is limited in scope, magnitude, and duration. Shocks to consumer confidence from the origin countries have no impact on future tourism demand. Our results offer important insights and implications for policymakers and tourist operators.

A combination selection algorithm on forecasting

“…(Dickey & Fuller, 1979), the Phillips-Perron (PP) test (Phillips & Perron, 1988), and the Hylleberg-Engle-Granger-Yoo (HEGY) test (Hylleberg et al, 1990) for a hypothesis of a seasonal unit-root which determines the nature of seasonal variation in the series. For examples, the ADF test is applied by Goh & Law (2002) and the PP test is applied by Gounopoulos, Petmezas and Santamaria (2012). The hypothesis ADF, PP and HEGY tests are presented in formula (12), and the results of the ADF, PP (using Eview) and HEGY (using R) are illustrated in Table 1.…”

Common trends in international tourism demand: Are they useful to improve tourism predictions?

“…A growing body of literature has focused on tourism demand forecasting, but most research efforts apply conventional forecasting methods, either casual econometric models (Cortés-Jiménez & Blake, 2011;Page, Song, & Wu, 2012, Lin, Liu & Song, 2015, time series models (Chu, 2008(Chu, , 2011Assaf, Barros, & Gil-Alana, 2011;Gounopoulos, Petmezas, & Santamaria, 2012), or a combination of them (Shen, Li, & Song, 2008;Coshall & Charlesworth 2010). See Song, Dwyer, Li and Cao (2012) and Peng, Song, and Crouch (2014) for a thorough review of tourism economics research and tourism demand forecasting studies.…”

“…Song and Li (2008) have acknowledged the importance of applying new approaches to tourism demand forecasting in order to improve the accuracy and the performance of the methods of analysis. Whilst most research efforts focus on conventional tourism forecasting methods (Gounopoulos, Petmezas, & Santamaria, 2012) or a combination of them (Chan, Witt, Lee, & Song, 2010), in recent years the availability of more advanced forecasting techniques and the requirement for more accurate forecasts of tourism demand have led to a growing interest in Artificial Intelligence (AI) techniques (Wu, Law, & Xu, 2012;Cang, 2013;Pai, Hung, & Lin 2014). The suitability of AI models to handle nonlinear behaviour is one of the reasons why Artificial Neural Networks (ANNs) are increasingly used for forecasting purposes.…”

Comparative analysis of exponential smoothing models to tourists' arrivals in Serbia

“…The tourism industry is one of the most crucial sectors for a thriving economy as it accounts for a large part of some countries' Growth Domestic Product (GDP) and employment figures. Tourism is characterized by large variations in numbers on a yearly basis and, as a result, predicting future arrivals is a very difficult task (Gounoploulos et al, 2012). According to Nunkoo and Smith they involve comparative economic advantages in relation to the regions which do not develop tourism.…”