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Tourism Forecasting in South Africa – Some Perspectives

Tourism Forecasting in South Africa – Some Perspectives. Andrea Saayman North-West University, Potchefstroom Campus. Agenda. Some facts about tourism to South Africa Review of academic studies Neural networks Pure time series forecasts ARDL forecasts VEC and TVP forecasts

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Tourism Forecasting in South Africa – Some Perspectives

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  1. Tourism Forecasting in South Africa – Some Perspectives Andrea Saayman North-West University, Potchefstroom Campus

  2. Agenda • Some facts about tourism to South Africa • Review of academic studies • Neural networks • Pure time series forecasts • ARDL forecasts • VEC and TVP forecasts • Seasonality in tourist arrivals • Current challenges

  3. Some facts about tourism to SA • The sanction years: • Domestic tourism focus • International tourism stagnation • Stagnant years: • Total tourist arrivals 1980 – 702 794 • Total tourist arrivals 1990 – 1 029 094 • Average growth in arrivals 4.3% per year • Change started in 1991/2: • Tourist arrivals in 1992 – 2 891 721 • Peak arrivals in 2008 – 9 728 860

  4. Time series of tourist arrivals

  5. African versus intercontinental tourists

  6. South Africa’s top 15

  7. Tourism’s growing importance in the economy

  8. Review of academic studies • Only a handful of academic papers on forecasting tourist arrivals • Focusing only on intercontinental tourist arrivals • 2001 – Burger et al. using neural networks • 2010 – Saayman & Saayman comparing pure time series forecast accuracy • 2012 – Louw & Saayman using ARDL models • 2012 – Botha & Saayman comparing TVP and VEC forecasts

  9. Neural networks • A practitioner’s guide • Case study of US tourist demand for city of Durban (1992-1998) • Compared time-series methods with neural networks • Back-propagation algorithm with momentum used to train process

  10. Neural networks

  11. Univariate forecasts • Compared accuracy of univariate time-series forecasts for tourist arrivals from top 5 intercontinental markets • Monthly arrivals from 1994 to 2006 • Ex post forecasts for 2007

  12. Comparison based on MAPE

  13. SARIMA forecasts

  14. Univariate forecasts • More accurate forecasts of overseas arrivals in SA with techniques that account for seasonality • SARIMA forecasts outperform others, including Holt-Winters • Non-seasonal ARIMA-models perform poorly in this context • Policy application remains limited

  15. ARDL forecasts • Forecasted arrivals from Asia, Europe, South America, North America, Australasia and UK • Ex post forecasts – 1 to 3 year horizon • Quarterly data from 1994 to 2004 • ARDL model with ECM • Included income, travel cost, price, infrastructure variables

  16. Results – forecasting

  17. Results – forecasting

  18. ARDL forecasts • Long run – real GDP per capita, real price and infrastructure significant • Demand is income elastic over both short and long run • Infrastructure only creates long run benefit • Demand is relative price inelastic over both short and long run • Transport cost has relatively small effect • Forecast accuracy: • Accuracy good for 1-year horizon • UK and Asia models presented best results

  19. Forecast models for arrivals from continents Quarterly data from 1994 to 2009 Ex ante forecasts for 1 year (over FIFA WC) VECM form benchmark model Compare TVP-LRM and TVP-ECM specification Used AR form of transition equation ECM and TVP forecasts

  20. Forecasting accuracy

  21. Demand Elasticities: North America

  22. Intercontinental tourist arrivals to South Africa Income elastic, but price inelastic destination Comparing methods: VECM superior in more stable environment TVP-LRM superior when gradual adjustment or shock long ago TVP-ECM superior in short-term shock situations Demand elasticities is becoming more consistent ECM and TVP forecasts

  23. Seasonality in intercontinental arrivals • SARIMA models outperform other non-seasonal models • 2009 paper by Shen, Li & Song • Deterministic seasonal dummies • Stochastic treatment of seasonality

  24. Current challenges • Forecasts for SA done by WTTC • Econex forecasted on ad-hoc basis using ARDL • A need for: • More continuous forecasts • More inclusive forecasts • Focusing on more than arrivals “Travel and Tourism research and forecasting in South Africa needs significant improvement, both in terms of quantity and quality”

  25. Current challenges • There is a need for: • A dedicated tourism forecasting unit • Austrian WIFO • Australian forecasting committee • Wider scope to serve a variety of industry needs • Skills development in forecasting • Better co-operation between all parties

  26. Thank you

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