پیش بینی پروازهای ورودی گردشگری در یونان و تاثیر شوک های اقتصاد کلان از کشورهای مبدا گردشگران

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.

The tourism industry is, for some countries, one of the most crucial sectors of their economy, as it accounts for a large part of their 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. Forecasts of tourist arrivals are essential for planning, policy making and budgeting purposes by tourism operators (Uysal & O’Leary, 1986). In response to this, a growing body of literature has focused on tourism demand and arrivals’ forecasts in several countries (for instance, Law (2000) for Taiwan and Hong Kong, Burger et al. (2001) for South Africa, Chu (2008) for nine major tourist destinations in the Asian-Pacific region, Dharmaratne (1995) and Dalrymple and Greenidge (1999) for Barbados, González and Moral (1996) for Spain, Chu, 2004, Song and Witt, 2006 and Chu, 2009 for Asian-Pacific countries, Lim and McAleer, 2001 and Athanasopoulos and Hyndman, 2008 for Australia, Smeral and Weber (2000) and Papatheodorou and Song (2005) for international tourism trends and Shen, Li, and Song (2010) for the United Kingdom outbound tourism demand) under the research framework that the tourism industry is a key sector in the economic development strategy of many developing countries. A second strand of literature that has emerged in recent years is the use of macroeconomic factors to explain tourism demand using structural time series models. For instance, Metzgen-Quemarez (1990) used real GDP figures from the United States, amongst other factors; Var, Golam, and Icoz (1990) and Icoz, Kozak, and Var (1998) considered Turkish Consumer Price Index (CPI) figures and the Turkish Lira currency exchange rate against the currency units from the tourists’ country of origin, respectively; Greenidge (2001) used real GDP and CPI of the country of origin as well as the price index of tourism in Barbados and finally, Song, Li, Witt, and Athanasopoulos (2010) employed GDP data of the country of origin and CPI in Hong Kong relative to the country of origin adjusted by the exchange rate. This paper seeks to break new ground by analyzing, for first time in the literature, the impact of macroeconomic shocks from the country of origin on future short term tourism demand to Greece. We examine the effect of tourists’ cost of living, unemployment and consumer confidence in the country of origin as the source of macroeconomic shocks. Particularly, the two latter variables have not been considered in the prior related literature. Tourists’ cost of living is used as a measure of price competitiveness of the destination and, as such, has a major impact on tourism demand. Unemployment and the consumer confidence indicator serve as useful proxies for the state of the economy in the origin country, which implies an impact to future demand for tourism. The intuition behind unemployment lies in two avenues of research which have focused on the wage curve hypothesis and the psychological impact on the level of happiness. Both explanations imply a negative impact on future tourism numbers in periods of high unemployment. The consumer confidence indicator reflects the level of economic uncertainty and/or expectations on future income and the level of precautionary savings. The build up of precautionary savings feeds into falling levels in tourism demand as consumers postpone or cancel vacations. Additionally, no study to our knowledge has attempted to forecast future arrivals in Greece, which is one of the most popular tourist destinations worldwide. According to the National Statistical Service of Greece, in 2002 the country welcomed 14.9 million international tourists placing Greece the 12th place most visited destination internationally. This yielded an income of $9.74 billion, boosting Greece in the top ten in the world. It is therefore of paramount importance for policy makers and industry that forecasting models are developed and tested to provide an accurate and reliable picture of future tourism arrivals in Greece. As a result, unlike previous studies, we use an array of forecasting models to generate short term predictions on tourism arrivals in Greece. The identification and analysis of the impact of macroeconomic shocks from the country of origin on tourism flows in Greece introduces an added dimension by recognizing the main source of risk to future arrivals. In this paper, we identify the potential risk coming from unemployment, tourists’ cost of living, defined as the CPI of Greece relative to the CPI of the country of origin, and the consumer confidence indicator.1 Although relative CPI has been used in past papers as a driver of tourism demand (Abbas and Ali-Ibrahim, 2011 and Song et al., 2010), no study has considered the impact of unemployment and consumer confidence as macroeconomic inputs. Macroeconomic shocks from these factors are introduced into a Vector Autoregressive (VAR) system of equations from which one can gauge the reaction and time it takes to impact on future arrivals. We utilise impulse response function within a VAR framework by simulating impulse response function within a VAR framework. This involves simulating impulse responses from the macroeconomic shock to provide information on the size of the reaction and the duration of the effects on future arrivals. Confidence bands are computed using Monte Carlo Simulation to determine the statistical reliability of the response. Our results reveal a number of interesting observations. First, preliminary analysis reveals that the ARIMA (1, 1, 1) model outperforms the double exponential smoothing and the Holt’s exponential smoothing model with trend as a short-term directional forecasting tool. However, the success rate of the ARIMA model in capturing long term trends does not translate into forecasting accuracy. Instead, based on the Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) measures, the Holt’s exponential smoothing approach is the best performing model as point forecasting tool. The next set of results focused on how random macroeconomic shocks, introduced into a system of equations, could impact on future tourism demand in the short term. Initial findings show that random unemployment shocks, as well as shocks to the tourists’ cost of living, have profound, yet time varying, effects on short term tourism demand in Greece. In contrast, shocks to consumer confidence from the country of origin have a benign impact on tourism demand. Closer inspection of the results indicate that despite the lack of diversification in the sources of tourism demand to Greece, downside risk in the two main countries of origin, the United Kingdom and Germany, is limited. To sum up, this paper has two main contributions to the literature. First, we consider tourist arrivals in Greece and provide a preliminary analysis on initial short term forecasts in future tourism demand. Given the importance of the tourism industry in Greece and the level of tourism demand, this addresses a major gap in the literature. The second contribution, and one that forms the overriding objective of this paper, is that it explores the impact of macroeconomic shocks of various sources from the country of origin on future tourism demand. The rest of the paper is structured as follows. Section “Related literature” reviews the related literature on the importance of the macroeconomic variables used in this study. Section “Data” discusses the data used and provides descriptive statistics. Section “Methodology” presents the methodology. Section “Empirical results” analyses the empirical results, followed by a discussion of the findings in Section “Further discussion of the results”. Section “Implications on tourist arrivals” discusses the implications of this study. Finally, Section “Conclusion” summarises and concludes the paper.

This paper opens a new avenue of research in tourism forecasting by investigating the impact of random macroeconomic shocks on short term forecasts for tourist arrivals. This approach differs from the established literature of investigating the demand function for tourists. Our study addresses a major gap in the literature by forecasting tourism arrivals in Greece. Forecasting tourists’ arrivals comes in two levels; first, we utilize an array of forecasting models, firmly established in the literature, to generate one year out of sample forecasts. Secondly, we estimate a VAR system from which we introduce random macroeconomic shocks to gauge the reaction of future tourism arrivals in terms of the sign, magnitude and duration. Macroeconomic factors used include unemployment, tourists’ cost of living, and finally, the consumer confidence indicator with the first and third variables acting as proxies for the state of the economy in the origin country. Undertaking such an exercise breaks new ground by identifying the greatest source of the risk to future tourism demand, both in terms of the variable and location. To begin with, our forecasting results were mixed. According to the preliminary analysis, the ARIMA (1,1,1) model outperforms other exponential smoothing models as a directional forecasting tool. This finding is robust on an ever expanding estimation period. However, the directional forecasting performance of the ARIMA fails to translate into forecasting accuracy. Instead, the Holt’s exponential smoothing model with trend generates the most accurate forecasts. In identifying the source of risk, we established that the source of tourists’ arrivals to Greece is undiversified and heavily over-weighted towards the United Kingdom and Germany. Despite this, the impulse response analysis yielded some interesting results. Consistent with the implications of previous studies on unemployment, there is some evidence that shocks have an immediate temporary negative impact on future tourists’ arrivals that is reversed four to six months after the shock. Furthermore, future tourists’ arrivals appear to react to shocks on the tourists’ cost of living, regardless of the country of origin, subject to a lag of three to four months. This contrasts with the lack of response in future tourism demand to a shock in consumer confidence index in the origin country. Taken together, despite the undiversified nature in the source of tourism flow to Greece, the impulse response results suggest that downside risk to future arrivals is limited, at least in the short term. The impulse response results presented in this study open a new dimension in the type of macroeconomic factors used and how these shocks impact on tourism demand in the future. As a result, our comprehensive evidence offers important implications and insights to policymakers and tourist operators regarding future tourism demand.