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on Tourism Economics |
| By: | Oscar Claveria (Faculty of Economics, University of Barcelona); Enric Monte (Department of Signal Theory and Communications, Polytechnic University of Catalunya (UPC)); Salvador Torra (Faculty of Economics, University of Barcelona) |
| Abstract: | This paper aims to compare the performance of different Artificial Neural Networks techniques for tourist demand forecasting. We test the forecasting accuracy of three different types of architectures: a multi-layer perceptron, a radial basis function and an Elman network. We also evaluate the effect of the memory by repeating the experiment assuming different topologies regarding the number of lags introduced. We used tourist arrivals from all the different countries of origin to Catalonia from 2001 to 2012. We find that multi-layer perceptron and radial basis function models outperform Elman networks, being the radial basis function architecture the one providing the best forecasts when no additional lags are incorporated. These results indicate the potential existence of instabilities when using dynamic networks for forecasting purposes. We also find that for higher memories, the forecasting performance obtained for longer horizons improves, suggesting the importance of increasing the dimensionality for long term forecasting. |
| Keywords: | tourism demand; forecasting; artificial neural networks; multi-layer perceptron; radial basis function; Elman networks; Catalonia. JEL classification: L83; C53; C45; R11 |
| Date: | 2013–11 |
| URL: | http://d.repec.org/n?u=RePEc:aqr:wpaper:201313&r=tur |
| By: | Johane Dikgang and Edwin Muchapondwa |
| Abstract: | This paper estimates the visitation demand function for Kgalagadi Transfrontier Park (KTP) in order to determine the conservation fee to charge international tourists to maximise park revenue. International tourists account for approximately 20 percent of total number of visitors to South African national parks, with domestic visitors making-up the remaining portion. Though small, the South African international tourism market is mature, and accounts for a disproportionately large share of net revenue. The random effects Tobit model is used to estimate visitation demand at the KTP and three other national parks. Using the estimated elasticities, the revenue-maximizing daily conservation fees are computed to be R1 131.94 (US$144.20) for KTP, R575.67 (US$73.33) for Kruger National Park (KNP), R722.95 (US$92.10) for Augrabies Falls National Park (AFNP) and R634.11 (US$80.78) for Pilanesberg National Park (PNP). Our findings therefore imply that the conservation fees of R180 (US$22.93) for KTP and KNP, R100 (US$12.74) for AFNP, and R45 (US$5.73) for PNP currently charged to international visitors are significantly lower. This indicates that international park fees could be raised. |
| Keywords: | conservation fee, demand, land claim, national park |
| Date: | 2013 |
| URL: | http://d.repec.org/n?u=RePEc:rza:wpaper:393&r=tur |