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ANALYSIS OF TOURIST FLOWS FROM RUSSIAN FEDERATION TO THE COUNTRIES OF THE EUROPEAN UNION

ANALYSIS OF TOURIST FLOWS FROM RUSSIAN FEDERATION TO THE COUNTRIES OF THE EUROPEAN UNION. Kirill Furmanov Olga Balaeva Marina Predvoditeleva National Research University Higher School of Economics. H igher School of Economics , Moscow , 2011 www.hse.ru.

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ANALYSIS OF TOURIST FLOWS FROM RUSSIAN FEDERATION TO THE COUNTRIES OF THE EUROPEAN UNION

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  1. ANALYSIS OF TOURIST FLOWSFROM RUSSIAN FEDERATIONTO THE COUNTRIES OF THE EUROPEAN UNION Kirill Furmanov Olga Balaeva Marina Predvoditeleva National Research University Higher School of Economics Higher School of Economics,Moscow, 2011 www.hse.ru

  2. FACTORS, AFFECTING THE NUMBER AND FREQUENCY OF TOURIST TRIPS MADE BY RUSSIAN CITIZENS INTERNATIONALLY • Visa-free regime/negotiations on visa system simplification; • Increasing amount of tour operators and agencies; • Development of Russian relative and supporting services; • Development of ICT; • Increase of the income of Russian householders; • Health lifestyletrend; • Long holidays; • Climate; • Interest in getting acquainted with new, different cultures. photo photo photo Higher School of Economics, Moscow, 2011

  3. NUMBER OF TOURIST TRIPS MADE BY RUSSIAN CITIZENS TO THE NON-CIS AND EU COUNTRIES (IN THOUSANDS) photo photo photo Higher School of Economics, Moscow, 2011

  4. SHARE OF TOURIST TRIPS MADE BY RUSSIAN CITIZENS TO THE NON-CIS COUNTRIES

  5. GROUPS OF THE EU COUNTRIES ACCORDING TO THE SHARE IN THE TOTAL TOURIST FLOW FROM RUSSIA TO THE EUIN 2010 photo photo photo Higher School of Economics, Moscow, 2011

  6. FORECASTING:DATA & METHODOLOGY (1) Data available: annual data on number of tourist trips from Russia to the countries of EU, 2000-2010 (11 observations) -> small sample! Parsimony is crucial! Models used: - Holt model (exponential smoothing with trend), - Box-Jenkins ARIMA photo photo photo Higher School of Economics, Moscow, 2011

  7. FORECASTING: DATA & METHODOLOGY (2) Our choice: Holt model Reason: non-stability of time series for manycountries of destination Empirical evidence: MSE for Holt model is lower for most destinations. In cases when ARIMA performs better, the forecasts obtained by ARIMA and Holt models are essentially similar

  8. FORECASTING: DATA & METHODOLOGY (3) • Including explanatory variables into ARIMA: • consumer price indices for the destination country and for EU area, • real money income index in Russia, • exchange rates. • -> no significant improvement • Why? • Inappropriateness of aggregate data due to heterogeneity of Russian consumers, • CPI is a poor proxy for tourism prices. photo photo photo Higher School of Economics, Moscow, 2011

  9. FORECAST FOR ALL EU COUNTRIES Number of tourist trips from Russia to European Union photo photo photo increase by 16% expected (2013 to 2010) Higher School of Economics, Moscow, 2011

  10. FORECASTS FOR THE MOST POPULAR DESTINATIONS (1) Finland Germany

  11. FORECASTS FOR THE MOST POPULAR DESTINATIONS (2) Italy Spain

  12. 2010 VS. 2013 CHANGES BETWEEN THE GROUPS 2010 Group 1 (Leaders) Group 2 Group 3 2013 Group 1 (Leaders) Group 2 Group 3 photo Spain Greece photo Some minor changes within groups 2 & 3 photo Higher School of Economics, Moscow, 2011

  13. LIMITATIONS AND FUTURE RESEARCH (1) photo photo photo Higher School of Economics, Moscow, 2011

  14. LIMITATIONS AND FUTURE RESEARCH (2) photo photo photo Higher School of Economics, Moscow, 2011

  15. Thank you Grazie K. Furmanov: furmach@menja.net O. Balaeva: obalaeva@hse.ru M. Predvoditeleva: mpredvoditeleva@hse.ru

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