1. Res. Asst. Berhan ÇOBAN Prof. Dr. Esin FİRUZAN
2017 International Conference on Sustainable Tourism Management
The tourist arrivals to Turkey from EU countries: convergence, cointegration and causality
Res. Asst. Berhan ÇOBAN Prof. Dr. Esin FİRUZAN
Dokuz Eylül University
Department of Statistics
Izmir - Turkey

2. Motivation The purpose of this study is to examine
convergence hypothesis in Turkey’s tourism markets from European Unions (EU)
cointegration and causality structure
monthly data over the period 1996–2016.
International tourist arrivals from 26 European Countries markets to Turkey.
DATA
Test for Convergence
Test for Cointegration & Causality
Structural Breaks
Volatility Model
Dickey Fuller
Gregory Hansen
Lee-Strazicich (LM)
RALS based
RALS- LM
Granger Causality

3. Contents 1 2 3 4 An overview of the Turkey Tourism
Convergence – Cointegration & Causality 2
Application of Tourism Markets 3
Conclusion 4

4. An overview of the Turkey Tourism
Over the past decades especially last 20 years, the importance of the tourism industry has been consistently increasing for many countries’ economy in the world as well as Turkey.
Labour force
Source of foreign exchange
Balance of payments
Economic growth
Turkey is a bridges between the Asian and European countries. In respect to the tourism industry, Turkey has a crucial potential and a huge share in the world tourism industry, along with other Mediterranean countries.

5. An overview of the Turkey Tourism
Turkey tourism sector is now among the top 10 countries in respect of international tourist arrivals. For instance, Turkey ranked sixth with respect to international tourist arrivals in 2015 with 39.7 million $.(UNWTO)
Germany
France
Bulgaria
UNWTO: United NationsWorld Tourism Organization

6. Convergence
Narayan (2006), convergence may be defined as the reduction in the difference between the log of total visitor arrivals and log of visitor arrivals from a particular country to Turkey and convergence is achieved when this difference approaches zero. (Narayan (2006))
VAt,Turkey and VAit are the total international visitor arrivals to Turkey at time t and visitor arrivals to Turkey from country i at time t, respectively.
If Yit from a particular country should be stationary. This means that tourism market is convergent.

7. Convergence
Importance of testing the convergence hypothesis in the tourism industry such as
it is helpful for policy makers in making marketing policy in a targeted tourism source market;
it is helpful in measuring the effectiveness of marketing policy by identifying the source markets
it is important to know whether or not visitor arrivals from new smaller markets
As a result convergence hypothesis useful in planning future marketing strategies.

8. Cointegration Cointegration analysis
long term linear correlation between time series.
linear model between two or more nonstationary series
stationarity feature of error terms produced by this model
Cointegration Tests,
Engle-Granger,
Johansen Juselius,
Gregory-Hansen
Westerlund-Edgerton test
RALS Based tests
In application, most of the working time series have structural breaks and heteroscedasticity.

9. Structural Break- Heteroscedasticity
Structural break may occur
policy changes financial crises natural disasters
These changes in the series, without any exact definition, are generally called as the change in the model parameters.
Linear regression model, error variance is assumed to be constant. However,
excess kurtosis unexpected volatility periods.
This changeability in error term influence model parameter estimates.
Structural breaks and Heteroscedasticity may yield
biases and inconsistent estimation results
inefficient parameter estimation

10. Structural Break- Heteroscedasticity
Most of conventional unit root and cointegration tests do not take in to account possible structural breaks and heteroscedastic error term.
Researches should be take into account this features when use the unit root and cointegration analysis.
Unit Root Tests for Convergence
The suitable process to test of convergence hypothesis is unit root analysis.
If the null hypothesis of a unit root cannot be rejected, the series is non-stationary and there is no convergence of tourism markets.
Ho: Specific tourism market are not convergence
HA: Specific tourism market are convergence. Series is stationary.

11. Unit Root Tests for Convergence
Classical unit root test
Dickey-Fuller (ADF)
There are different unit root test account for structural breaks
Zivot and Andrews (1992) (ZA)
Vogelsang and Perron (1998) (VP)
Lee and Strazicich (2003, 2004) (LM)
Im, Lee, and Tieslau (2014) (RALS-LM)
To improve the power of the LM test, Im, Lee, and Tieslau (2014). adopt the procedure to utilize the information on non-normal errors and volatility.
They adopt the “residual augmented least squares” (RALS) method.

12. RALS-LM Tests for Convergence
The regression equation for the LM test may be written as follows
where the t-statistic for is denoted by indicates the de-trended series, Zt is a vector of exogenous variables, is a vector of parameters is a white noise process with classical properties;
The test developed by Meng and Lee (2012) augments the term while testing
regression , using the residual augmented least squares (RALS) methods. The term is estimated as follows:

13. RALS-LM Tests for Convergence
Further, the second and third moments of are included in to use information of non-normal errors and following regression is estimated to obtain the RALS-LM test statistic denoted as

14. Gregory – Hansen Coint. Test (GH)
According to Engle – Granger procedure,
The hypotheses for this test are;
GH test is similar to the Engle – Granger cointegration method. The breaks are tried to be determined by adding dummy variables to the Engle - Granger method.
MODEL A Level Shift (C),

15. Gregory – Hansen Coint. Test (GH)
MODEL B Level Shift with trend (C/T)
MODEL C Regime Shift (C/S )
The dummy variable
Here, T represents the number of observations, while is a coefficient which shows the break period between (0.15T, 0.85T) and takes the value of 0 or 1.

16. RALS Based Coint. Test
The RALS Cointegraiton test is developed to test for null of no cointegration when the error term of a regression shows signs of skewness and excess kurtosis.
Im and Schmidt(2008) show that deviations from normality can be taken in to account by augmenting with two new regressors that correct for skewness and excess kurtosis and transform equation into the following RALS regression equation

17. Application of Tourism Markets
We used international tourist arrivals to Turkey from each of Turkey’s 26 European Unions (EU) markets. We use monthly data over the period 1996–2016.
These markets are:
Germany
France
Poland
Austria
Netherlands
Romania
Belgium
England
Greece
Bulgaria
Sweden
Croatia
Denmark
Italy
Slovenia
Lithuanian
Finland
Portugal
Latvia
Luxembourg
Ireland
Estonia
Spain
Czech Republic
Hungary
Slovakia
Tourist arrivals of these countries comprise %70 of total arrivals of Turkey Tourism markets.

18. Application of Tourism Markets
Tourist Arrivals for EU markets
strong seasonality in monthly tourist arrivals data was detected

19. The plots of log of tourist arrivals ratio for some country
level and trend breaks stand out in most series along with an upward or a downward trend.

20. The plots of log of tourist arrivals ratio for some country
level and trend breaks stand out in most series along with an upward or a downward trend.

21. Application of Tourism Markets
Tourist arrivals ratio for all countries have
volatility component and structural breaks 10 12 13 17
Number of convergent markets

22. Application of Tourism Markets
* One break convergence 17 20
Number of convergent markets
Natural log of the tourist arrivals ratio could be rejected in 20 out of 26 countries under both the RALS-LM
France,Netherland, Croatia, Ireland and Italy are not convergent

23. Application of Tourism Markets
There are some cointegrated relation between different market and total
tourist arrivals for Turkey with different structural break model 16 10 24
Number of Cointegrated markets

24. Application of Tourism Markets
shows Granger cause markets 15
shows does not Granger cause
markets 11
Null Hypothesis:
Markets does not Granger Cause total tourist arrivals

25. Break Point Analysis
The natural disaster, political and economic tensions may be one of the reasons of structural breaks between countries . Concerning the break dates, the first break dates mostly correspond to the second half of the 1990s.
For instance,
break date occurred related the Izmit earthquake on August 17,1999: Germany, Denmark, Romania, Slovenia, Latvia, Slovakia, France
break date occurred related SARS and avian flu, seen in Turkey in 2005: England, Estonia, Hungary, Luxemburg, Germany, Spain, Portugal, Lithuanian,
break date occurred related the 9/11 (world trade center) terrorist attacks: Greece, Belgium, Bulgaria, Poland
break date occurred related introduction of the euro as an accounting currency to the world financial markets:, Austria, Netherland, İtaly

26. Conclusion
Tourist arrivals data of Turkey comprise structural changes and volatility.
The study finds strong evidence that most of the tourism markets for Turkey are converging. İt could be asserted that the tourism policy of Turkey for these 20 convergent markets is successful and effective.
These convergent tourist markets make a contribution to the increase in tourist arrivals to Turkey
Based on these crucial break dates, we may assert that political, social, natural and economic events have crucial impacts on Turkey’s tourism.
In addition there is some long run and causality relationship between different tourism markets and total tourist arrivals in Turkey.

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28. Thank you for your attention