Forecasting tourist arrivals using origin country macroeconomics

ABSTRACT This study utilizes both disaggregated data and macroeconomic indicators in order to examine the importance of the macroeconomic environment of origin countries for analysing destinations’ tourist arrivals. In particular, it is the first study to present strong empirical evidence that both of these features in tandem provide statistically significant information of tourist arrivals in Greece. The forecasting exercises presented in our analysis show that macroeconomic indicators conducive to better forecasts are mainly origin country-specific, thus highlighting the importance of considering the apparent sharp national contrasts among origin countries when investigating domestic tourist arrivals. Given the extent of the dependency of the Greek economy on tourism income and also the perishable nature of the tourist product itself, results have important implications for policymakers in Greece.

[1]  F. Diebold,et al.  Comparing Predictive Accuracy , 1994, Business Cycles.

[2]  Yu Shan Wang,et al.  Effects of budgetary constraints on international tourism expenditures , 2014 .

[3]  J. L. Eugenio-Martin,et al.  Economic crisis and tourism expenditure cutback decision , 2014 .

[4]  Valentina Colombo Economic policy uncertainty in the US: Does it matter for the Euro area? , 2013 .

[5]  Wensheng Kang,et al.  Structural oil price shocks and policy uncertainty , 2013 .

[6]  Shui Ki Wan,et al.  AGGREGATE VS. DISAGGREGATE FORECAST: CASE OF HONG KONG , 2013 .

[7]  George Filis,et al.  Dynamic Co-Movements of Stock Market Returns, Implied Volatility and Policy Uncertainty , 2013 .

[8]  George Filis,et al.  Oil prices, tourism income and economic growth: A structural VAR approach for European Mediterranean countries , 2013 .

[9]  Chiang-Ming Chen,et al.  A comparison study of travel expenditure and consumption choices between first-time and repeat visitors , 2013 .

[10]  S. Davis,et al.  Measuring Economic Policy Uncertainty , 2013 .

[11]  Tianshu Zheng,et al.  How do less advanced forecasting methods perform on weekly RevPAR in different forecasting horizons following the recession , 2012 .

[12]  Haiyan Song,et al.  Tourism Demand Modelling and Forecasting , 2012 .

[13]  S. Leduc,et al.  Uncertainty Shocks Are Aggregate Demand Shocks , 2012 .

[14]  Daniel Santamaria,et al.  Forecasting tourist arrivals in Greece and the impact of macroeconomic shocks from the countries of tourists' origin. , 2012 .

[15]  Haiyan Song,et al.  Assessing the Impacts of the Global Economic Crisis and Swine Flu on Inbound Tourism Demand in the United Kingdom , 2012 .

[16]  S. Lee,et al.  Do expectations of future wealth increase outbound tourism? Evidence from Korea , 2012, Tourism Management.

[17]  Kamran Shahanaghi,et al.  Tourist arrival forecasting by evolutionary fuzzy systems. , 2011 .

[18]  Pietro Veronesi,et al.  Political Uncertainty and Risk Premia , 2011 .

[19]  Haiyan Song,et al.  Impact of financial/economic crisis on demand for hotel rooms in Hong Kong , 2011 .

[20]  J. Brida,et al.  Research Note: Tourism Demand Forecasting with SARIMA Models – the Case of South Tyrol , 2011 .

[21]  Gang Li,et al.  Combination forecasts of international tourism demand , 2011 .

[22]  Stavros Degiannakis,et al.  ARCH Models for Financial Applications , 2010 .

[23]  Joaquín Alegre,et al.  An analysis of households' appraisal of their budget constraints for potential participation in tourism. , 2010 .

[24]  Jaume Rosselló,et al.  Global Economic Crisis and Tourism: Consequences and Perspectives , 2010 .

[25]  John T. Coshall,et al.  Combining volatility and smoothing forecasts of UK demand for international tourism , 2009 .

[26]  G. C. D. O. Santos Research Note: Forecasting Tourism Demand by Disaggregated Time Series – Empirical Evidence from Spain , 2009 .

[27]  Helmut Lütkepohl,et al.  The role of the log transformation in forecasting economic variables , 2009, SSRN Electronic Journal.

[28]  Michael McAleer,et al.  ARMAX modelling of international tourism demand , 2009, Mathematics and Computers in Simulation.

[29]  Yu-shan Wang,et al.  The impact of crisis events and macroeconomic activity on Taiwan's international inbound tourism demand , 2008, Tourism Management.

[30]  George E. P. Box,et al.  Time Series Analysis: Box/Time Series Analysis , 2008 .

[31]  Haiyan Song,et al.  Tourism demand modelling and forecasting—A review of recent research , 2008 .

[32]  M. Saayman,et al.  Determinants of Inbound Tourism to South Africa , 2008 .

[33]  H. N. Rudež The GDP impact on international tourism demand: a Slovenia based case. , 2008 .

[34]  Kevin K. F. Wong,et al.  Tourism forecasting: To combine or not to combine? , 2007 .

[35]  Z. Schwartz,et al.  Data patterns and the accuracy of annual tourism demand forecasts. , 2007 .

[36]  Karl Taylor,et al.  Business Cycles and the Role of Confidence: Evidence for Europe , 2007 .

[37]  L. Turner,et al.  Regional Data Forecasting Accuracy: The Case of Thailand , 2006 .

[38]  Albert Sesé,et al.  Designing an artificial neural network for forecasting tourism time series , 2006 .

[39]  Haiyan Song,et al.  Bayesian models for tourism demand forecasting. , 2006 .

[40]  George Athanasopoulos,et al.  Modelling and Forecasting Australian Domestic Tourism , 2006 .

[41]  Youcheng Wang,et al.  Examining and identifying the determinants of travel expenditure patterns , 2006 .

[42]  Haiyan Song,et al.  Forecasting international tourist flows to Macau , 2006 .

[43]  Bernardina Algieri An Econometric Estimation of the Demand for Tourism: The Case of Russia , 2006 .

[44]  Manuel Vanegas,et al.  An econometric study of tourist arrivals in Aruba and its implications. , 2005 .

[45]  L. Turner,et al.  Data Disaggregation in Demand Forecasting , 2005 .

[46]  Rachel J. C. Chen Before and after the inclusion of intervention events: an evaluation of alternative forecasting methods for tourist flows. , 2005 .

[47]  Juan Luis Nicolau,et al.  Heckit modelling of tourist expenditure: evidence from Spain , 2005 .

[48]  B. Prideaux Factors affecting bilateral tourism flows , 2005, Annals of Tourism Research.

[49]  Imad A. Moosa,et al.  Forecasting international tourist flows to Australia: a comparison between the direct and indirect methods , 2005 .

[50]  J. Nicolau,et al.  STOCHASTIC MODELING: A Three-Stage Tourist Choice Process , 2005 .

[51]  Sydney C. Ludvigson,et al.  Consumer Confidence and Consumer Spending , 2004 .

[52]  N. Dritsakis Cointegration analysis of German and British tourism demand for Greece , 2004 .

[53]  Satish Chandra,et al.  Applications of Multivariate Analysis in International Tourism Research: The Marketing Strategy Perspective of NTOs , 2004 .

[54]  V. Cho A comparison of three different approaches to tourist arrival forecasting , 2003 .

[55]  S. F. Witt,et al.  Forecasting the Demand for International Business Tourism , 2003 .

[56]  Jinhyung Chon,et al.  A forecasting model of tourist arrivals from major markets to Thailand. , 2003 .

[57]  Haiyan Song,et al.  Tourism forecasting: accuracy of alternative econometric models , 2003 .

[58]  Kevin K. F. Wong,et al.  Factors Affecting Demand for Tourism in Hong Kong , 2002 .

[59]  Rob Law,et al.  Modeling and forecasting tourism demand for arrivals with stochastic nonstationary seasonality and intervention. , 2002 .

[60]  V. Cho Tourism Forecasting and its Relationship with Leading Economic Indicators , 2001 .

[61]  Rob Law,et al.  A practitioners guide to time-series methods for tourism demand forecasting - a case study of Durban, South Africa , 2001 .

[62]  S. Poukliakova,et al.  Tourism demand modelling and forecasting: modern econometric approaches , 2001 .

[63]  Michael McAleer,et al.  Forecasting tourist arrivals , 2001 .

[64]  Michael McAleer,et al.  MONTHLY SEASONAL VARIATIONS: ASIAN TOURISM TO AUSTRALIA , 2001 .

[65]  Egon Smeral,et al.  Forecasting international tourism trends to 2010 , 2000 .

[66]  R. Law Back-propagation learning in improving the accuracy of neural network-based tourism demand forecasting , 2000 .

[67]  Haiyan Song,et al.  Tourism Demand Modelling And Forecasting: Modern Econometric Approaches , 2003 .

[68]  Kenneth Wilson,et al.  Modelling Business Travel , 2000 .

[69]  Rob Law,et al.  A neural network model to forecast Japanese demand for travel to Hong Kong , 1999 .

[70]  Gerard S. Dharmaratne,et al.  Forecasting tourist arrivals in Barbados , 1995 .

[71]  Richard A. Davis,et al.  Time Series: Theory and Methods (2Nd Edn) , 1993 .

[72]  P. Brockwell,et al.  Time Series: Theory and Methods , 2013 .

[73]  A. Bull,et al.  The Economics of Travel and Tourism , 1991 .

[74]  Byung Sam Yoo,et al.  Seasonal integration and cointegration , 1990 .

[75]  C. Propper An econometric estimation of the demand for private health insurance , 1987 .

[76]  P. Phillips Testing for a Unit Root in Time Series Regression , 1988 .

[77]  W. Newey,et al.  A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelationconsistent Covariance Matrix , 1986 .

[78]  M. Uysal,et al.  A canonical analysis of international tourism demand. , 1986 .

[79]  G. Box,et al.  On a measure of lack of fit in time series models , 1978 .

[80]  G. Schwarz Estimating the Dimension of a Model , 1978 .

[81]  H. Akaike A new look at the statistical model identification , 1974 .

[82]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.