Mustafa Özer

Faculty of Economics and Administrative Sciences, Anadolu University, Turkey

Inci Oya Coşkun

Faculty of Tourism, Anadolu University, Yunus Emre Campus 26470 Eskisehir, Turkey

Mustafa Kırca

Faculty of Economics and Administrative Sciences, Anadolu University, Turkey




Turkey is one of the top tourism destinations in the world and the tourism industry has become an indispensable source of income. The main inbound tourism market for Turkey is Europe with a 50% average of the total tourist arrivals followed by Russia and the Asian countries. Tourism is an important industry, especially for tourist receiving countries where tourism is a major source of foreign exchange earnings. As the foreign exchange earnings are directly related with the tourist expenditure, the effects of prices or more commonly the exchange rates should be considered in any demand study. Accordingly, this study attempts to reveal the time varying causal relationships between exchange rates and tourist arrivals for European inbound tourist markets. The time varying linkages between the nominal Euro exchange rate and tourist arrivals from the EU-15 countries (namely; Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain, Sweden, United Kingdom) to Turkey for the period 2002:01-2014:12 are investigated using time varying bootstrap analysis. The results indicate that time-varying causality is bidirectional for different periods and different countries, but existing for each tourism market.

Keywords: Tourism demand, Exchange rates, Turkey, Time varying bootstrap analysis.



International tourism plays an increasingly significant role in the world economy since 1950s. International tourist arrivals has increased from 25 million in 1950s to 1,1 billion in 2014 (UNWTO, 2015) with a total contribution of 9.8% to the global GDP (WTTC, 2015a). The growth of tourism in Turkey has pursued the same path and become evident in 1982 by means of the Tourism Encouragement Act, allowing government incentives for tourism investments.

Turkey has been one of the top tourism destinations in the world and attracted over 40 million tourists. The total contribution of tourism to the Turkish economy is 12% through 34.3 billion US$ direct tourism receipts (WTTC, 2015b; Ministry of Culture and Tourism, 2015).

Turkey receives tourists from all over the world, however the main inbound tourist market is Europe with a 52% of the total tourist arrivals followed by Russia (24%) and Asian countries (15%) by 2014 (Ministry of Culture and Tourism, 2015). Turkey is a popular destination for Europeans, because it is a nearby country with various attractions, relaxed visa regime and attractive exchange rate (Coskun and Ozer, 2011). Mostly, European markets have similar structures in terms of the purpose of travel, length and period of stay, visitor profile and expenditure. Tourists prefer Turkey for travel, entertainment, sportive or cultural activities with a share of 55% (Ministry of Culture and Tourism, 2009), and this assumption is valid for the Europeans as well.

Tourism substantially contributes to countries’ economies by generating GDP, creating employment and socio-economic development opportunities (Wu, Li & Song, 2012). Regarding the importance of tourism for a destination economy and its direct link to tourism-related businesses, tourism demand analysis has attracted increasing interest from researchers. According to Song et al. (2009), tourism demand is defined as ‘the quantity of tourism products that consumers are willing and able to purchase under a specified period and a given set of factors’. These set of factors, in other words, the determinants of tourism demand may vary from economic variables to cultural differences or cyclical circumstances and have been studied thoroughly in the tourism literature (Uysal & Crompton, 1984; Crouch, 1994 etc.). The results of these researches indicate that economic variables have greater impacts on tourism demand than other variables and the most distinctive one is income followed by relative prices and then the exchange rates (Zhang et al, 2011).

The microeconomic theory argues that demand is sensitive to prices. Various studies have demonstrated the price elasticity of tourism demand is considerably higher than unit elasticity (Içoz, Var & Kozak, 1997). Tourists compare market prices at the destination with the cost of living at home and substitute destinations. Relative price is the ratio of consumer price indexes between destination countries or substitutes and at home. However, as Crouch (1994) argued, tourists are generally not well informed in advance about price levels and price changes in destinations whereas they are reasonably well informed about the exchange rate mechanisms. With limited information on the price levels of destinations, tourists may have a tendency to respond to a change in exchange rates (Lee, 2012). The depreciation of a local currency will act as a decrease in the prices and stimulate international tourist arrivals (Wang et al., 2008). Conversely, appreciation of local currency will influence both tourist arrivals to the country and tourist departures from the country as well. Accordingly, exchange rates are used as a proxy to measure price levels of different destinations in general. Because they are easier to obtain information and to understand, even to compare alternative destinations for tourists than relative prices or consumer price indices (Crouch, 1993).

The aim of this paper is to investigate the time-varying causal relationships between Euro exchange rate and tourism demand from the EU-15 countries (namely; Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain, Sweden, United Kingdom) by using bootstrap Granger non-causality tests with fixed size rolling subsamples developed by Balcilar, Ozdemir & Arslanturk (2010). The study is expected to contribute to a better understanding of tourist behavior interacting with exchange rates, as a proxy for relative prices. The results can be used by researchers, as well as practitioners to determine efficient market and price strategies and national policies for the tourism industry.



Uysal & O’Leary (1986) suggest exchange rates can be used as an independent variable along with per capita income, relative prices and promotional expenditures to predict and analyze international tourism demand. Furthermore, Webber (2001) states that exchange rate volatility affects tourists’ destination choice and changes in the exchange rates are likely to have the same impact as relative price changes. That is the reason why exchange rate is a major determinant of tourist demand and exchange rate regimes with low uncertainty could promote tourism (Santana-Gallego et al, 2010; Wang et al., 2008). Fluctuating exchange rates can result in several different effects such as choosing a substitute destination or less traveling abroad, reducing the length of stay and the expenditures.

In tourism literature, there are many studies investigating the relationship between exchange rates and international tourism demand. These studies often argue whether fluctuations in the exchange rates effect demand or not by employing cointegration techniques, regression analysis and different methods. For example, Webber (2001) analyzed the long-run Australian outbound tourism demand for the period 1983Q1-1997Q4 for nine major tourism destinations by Johansen cointegration and Granger causality tests. The exchange rate volatility is found to be a significant determinant of the long-run tourism demand. Rosello, Aguilo & Riera (2005) modelled tourism demand for Balearic Islands from the UK and Germany by considering exchange rates as an independent variable, a determinant of tourism demand. Yap (2012) examined the effects of exchange rate volatility on Australian inbound tourism demand from 9 tourist generating countries by multivariate GARCH method for the period of 1991M1-2011M1 and found out appreciation/depreciation of a country’s currency has impacts on demand volatility. Yap (2013) also investigated the impacts of exchange rates on Australia’s domestic and outbound tourism demand using panel generalized least squares models and showed that the exchange rates influence both domestic and outbound travel decisions of the Australians.

Lee (2012) studied the causal relationship between foreign exchange rates and inbound/outbound tourism demand in South Korea. Johansen cointegration and Granger causality test were used for 1990M1-2010M9 monthly data. The results demonstrate there is a long-run relationship between exchange rates and inbound/outbound tourism demand. Also, exchange rates affect outbound tourism demand, but the inbound tourism was not affected. DeVita (2014) analyzed the impact of exchange rate regimes on international tourism flows of 27 countries over the period of 1980-2011 by employing SYS-GMM method. The findings of the study supports that maintaining a relatively stable exchange rate using right policy decisions, tourism demand may also kept stable. Corgel, Lane & Walls (2013) investigated the effects of exchange rates on hotel demand in the US using quarterly data for 1988Q1-2012Q1 with a single equation partial adjustment framework. The results support the hypothesis of exchange rates effect hotel demand on different scales. Tang et al. (2014) investigated the dependence between tourism demand and exchange rates for China’s inbound tourism demand using Copula-GARCH models using monthly data for the period 1994M1-2011M12. Among the studied six tourist generating countries, only Russia was found to be extremely sensitive to exchange rate volatility, but in general exchange rates were concluded not to be a determinant for the selected countries.

Var, Mohammad & Icoz (1990) modelled the factors effecting international tourism demand for Turkey by including exchange rates as an independent variable. Icoz, Var & Kozak (1997) analysed the determinants of tourism demand with multivaritate OLS based regression model. The results indicated that exchange rates have important effects on tourism demand. Akis (1998) also suggested exchange rates as a determinant of tourism demand in addition to a number of economic variables such as per capita income, prices in the host country and cost of travel. DeVita & Kyaw (2013) argues if the exchange rate is an indicator of Turkish inbound tourism demand from Germany using quarterly data for the period 1996-2009 by employing GARCH method. They conclude that exchange rates are significant determinants of tourism demand.

Nevertheless, the studies investigating the time-varying nature of this relationship are limited, and missing for Turkey in particular. Time-varying parameter (TVP) method is mostly used for examining the causal relationship between tourism demand/receipts and economic growth. Song & Wong (2003) proposed this new TVP approach to tourism demand modelling. This method ignores the restrictive assumptions of traditional methods assuming that the parameters remain constant over the sample period. They tested the appropriateness of the TVP approach to tourism demand modelling based on the data set of Hong Kong tourism demand from six major tourism origin countries, and confirmed that the method gave better results.

Li, Song & Witt (2006) developed time varying parameter (TVP) linear almost ideal demand system (LAIDS) to compare fixed parameter model results. The findings indicated that the TVP-LAIDS outperformed the traditional methods in case it allowed evolution of demand over time. Wu, Li & Song (2012) also analyzed the dynamics of consumption behavior of top four tourist markets for Hong Kong using annual data for the period 1984-2008 with TVP-AIDS model considering three major expenditure categories including shopping, hotel accommodation and meals outside hotels. Song et al. (2011) employed structural time series model (STSM) combining time-varying parameter (TVP) regression approach to forecast quarterly tourist arrivals to Hong Kong from four key source markets using quarterly data for the period 1985Q1-2008Q4. They compared seven different methods and STSM and TVP approach outperformed for ex post and ex ante forecasts.

Dragouni, Filis & Antonakakis (2013) employed VAR-based spillover index to investigate the time-varying relationship between tourism and economic growth for selected European countries using monthly data for the period 1995-2012. The results of the study indicates the relationship is not stable over time, exhibiting patterns during major economic events and these patterns are more apparent for some specific countries. Arslanturk, Balcilar & Ozdemir (2011) investigated the causal link between tourism receipts and economic growth using rolling window and time-varying coefficient estimation method for South Africa for the period 1960-2011. The results indicate bidirectional causality between tourism receipt and economic growth, basically the opposite of full sample VECM indicating no causality.

Arslanturk, Balcilar & Ozdemir (2011) compared time-varying coefficient model with VECM based Granger causality to determine the causal relationship between tourism receipts and GDP for the period 1963-2006 for Turkey. The results indicate that VECM based Granger causality does not exist, whilst time-varying coefficients model shows that tourism receipts can be used to predict GDP after the 1980s.

This study is expected to fill the gap in tourism literature and lead to a better understanding of the nature of tourist behavior with respect to the changes in the exchange rates, particularly for Turkey. Following section explains the methodology used for this purpose in detail.



In this study, we investigate the time-varying causal relationships between international tourist arrivals to Turkey from the EU-15 countries and the Euro exchange rate by using bootstrap Granger non-causality tests with fixed size rolling subsamples developed by Balcilar, Ozdemir & Arslanturk (2010). As mentioned in their study, if structural changes exist in the data, the examination of the causal relationships between variables cannot be adequate considering the full sample, since the dynamic linkages between variables can exhibit instability across different sub-samples.

In this approach, to test the causality relationship, Granger non-causality method was used. As is well known, a variable X does not Granger cause Y, if the past values of X does not help to predict Y. The Granger non-causality test is performed to determine whether the lagged values of X are jointly significant or not by carrying out joint restriction tests of the Wald, Lagrange multiplier (LM), and likelihood ratio (LR) statistics within the vector autoregression (VAR) framework. But, as indicated in Aye et al. (2014), to get valid results from the implementation of these tests, time series in question should be stationary. According to Balcilar & Ozdemir (2013), if the time series do not exhibit stationarity, then these tests may not have standard asymptotic distributions, creating difficulties in the levels estimation of VAR models.

To solve these problems, some solutions can be utilized. As is indicated by Balcilar, Ozdemir & Arslanturk (2010) and Aye et al. (2014), the first attempt to overcome these difficulties had been made Toda & Yamamoto (1995) and Dolado & Lutkepohl (1996) proposing a solution to obtain standard asymptotic distribution for the Wald test based on the estimation of an augmented VAR with I(1) variables, or the long-run causality test of VAR (p) coefficients. According to their solution, there has to be at least one unrestricted coefficient matrix under the null hypothesis to generate standard asymptotic distribution. However, Shukur & Mantalos (1997) showed that proposed Wald test does not exhibit the correct size in small and medium-sized samples after investigating the size and power properties of eight different versions of the Granger non-causality test in standard and modified form based on the Monte Carlo simulations. Shukur and Mantalos (2000) also suggested that the small sample corrected LR tests exhibit relatively better power and size properties, even for small samples.

Same Manto Carlo simulations indicated that the critical values may improve by applying the residual-based bootstrap (RBB) method because of the reason that the true size of the test in a system of one to ten equations converges its nominal value (Balcilar, Ozdemir & Arslanturk, 2010). The results of Mantalos & Shukur (1998) indicate that, in the absence of cointegration, all standard tests that do not use the RBB method perform inadequately, especially in small samples. Furthermore, according to Mantalos (2000), the bootstrap test possesses the best power and size in almost all situations, regardless of cointegration properties. Therefore, based on the findings and reasons stated so far, we prefer to use RBB based modified-LR statistic to examine the causal relationships between exchange rates and tourism demand.