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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

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

slide2

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.

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Higher School of Economics, Moscow, 2011

slide3

NUMBER OF TOURIST TRIPS MADE BY RUSSIAN CITIZENS TO THE NON-CIS AND EU COUNTRIES

(IN THOUSANDS)

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Higher School of Economics, Moscow, 2011

slide5

GROUPS OF THE EU COUNTRIES ACCORDING TO THE SHARE IN THE TOTAL TOURIST FLOW FROM RUSSIA TO THE EUIN 2010

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Higher School of Economics, Moscow, 2011

slide6

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

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Higher School of Economics, Moscow, 2011

forecasting data methodology 2
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

slide8

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.

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Higher School of Economics, Moscow, 2011

slide9

FORECAST FOR ALL EU COUNTRIES

Number of tourist trips from Russia

to European Union

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increase by 16% expected (2013 to 2010)

Higher School of Economics, Moscow, 2011

slide12

2010 VS. 2013 CHANGES BETWEEN THE GROUPS

2010

Group 1

(Leaders)

Group 2

Group 3

2013

Group 1

(Leaders)

Group 2

Group 3

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Spain

Greece

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Some minor changes within groups 2 & 3

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Higher School of Economics, Moscow, 2011

slide13

LIMITATIONS AND FUTURE RESEARCH (1)

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Higher School of Economics, Moscow, 2011

slide14

LIMITATIONS AND FUTURE RESEARCH (2)

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Higher School of Economics, Moscow, 2011

k furmanov furmach@menja net o balaeva obalaeva@hse ru m predvoditeleva mpredvoditeleva@hse ru

Thank you

Grazie

K. Furmanov: [email protected]

O. Balaeva: [email protected]

M. Predvoditeleva: [email protected]

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