“A multivariate neural network approach to tourism demand forecasting”
This study compares the performance of different Artificial Neural Networks models for tourist demand forecasting in a multiple-output framework. We test the forecasting accuracy of three different types of architectures: a multi-layer perceptron network, a radial basis function network and an Elman neural network. We use official statistical data of inbound international tourism demand to Catalonia (Spain) from 2001 to 2012. By means of cointegration analysis we find that growth rates of tourist arrivals from all different countries share a common stochastic trend, which leads us to apply a multivariate out-of-sample forecasting comparison. When comparing the forecasting accuracy of the different techniques for each visitor market and for different forecasting horizons, we find that radial basis function models outperform multi-layer perceptron and Elman networks. We repeat the experiment assuming different topologies regarding the number of lags used for concatenation so as to evaluate the effect of the memory on the forecasting results, and we find no significant differences when additional lags are incorporated. These results reveal the suitability of hybrid models such as radial basis functions that combine supervised and unsupervised learning for economic forecasting with seasonal data.
|Date of creation:||May 2014|
|Date of revision:||May 2014|
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- Daniel Santin & Francisco Delgado & Aurelia Valino, 2004. "The measurement of technical efficiency: a neural network approach," Applied Economics, Taylor & Francis Journals, vol. 36(6), pages 627-635.
- Kwiatkowski, Denis & Phillips, Peter C. B. & Schmidt, Peter & Shin, Yongcheol, 1992.
"Testing the null hypothesis of stationarity against the alternative of a unit root : How sure are we that economic time series have a unit root?,"
Journal of Econometrics,
Elsevier, vol. 54(1-3), pages 159-178.
- Kwiatkowski, D. & Phillips, P.C.B. & Schmidt, P., 1990. "Testing the Null Hypothesis of Stationarity Against the Alternative of Unit Root : How Sure are we that Economic Time Series have a Unit Root?," Papers 8905, Michigan State - Econometrics and Economic Theory.
- Denis Kwiatkowski & Peter C.B. Phillips & Peter Schmidt, 1991. "Testing the Null Hypothesis of Stationarity Against the Alternative of a Unit Root: How Sure Are We That Economic Time Series Have a Unit Root?," Cowles Foundation Discussion Papers 979, Cowles Foundation for Research in Economics, Yale University.
- MacKinnon, James G & Haug, Alfred A & Michelis, Leo, 1999.
"Numerical Distribution Functions of Likelihood Ratio Tests for Cointegration,"
Journal of Applied Econometrics,
John Wiley & Sons, Ltd., vol. 14(5), pages 563-77, Sept.-Oct.
- James G. MacKinnon & Alfred A. Haug & Leo Michelis, 1996. "Numerical Distribution Functions of Likelihood Ratio Tests for Cointegration," Working Papers 1996_07, York University, Department of Economics.
- Mackinnon, J.G. & Haug, A.A. & Michelis, L., 1996. "Numerical Distribution Functions of Likelihood Ratio Tests for Cointegration," G.R.E.Q.A.M. 96a09, Universite Aix-Marseille III.
- Jacint Balaguer & Manuel Cantavella-Jordá, 2000.
"Tourism As A Long-Run Economic Growth Factor: The Spanish Case,"
Working Papers. Serie EC
2000-10, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
- Jacint Balaguer & Manuel Cantavella-Jorda, 2002. "Tourism as a long-run economic growth factor: the Spanish case," Applied Economics, Taylor & Francis Journals, vol. 34(7), pages 877-884.
- Francis X. Diebold & Robert S. Mariano, 1994.
"Comparing Predictive Accuracy,"
NBER Technical Working Papers
0169, National Bureau of Economic Research, Inc.
- Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-63, July.
- Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-44, January.
- Madden, Gary G & Tan, Joachim, 2008.
"Forecasting international bandwidth capacity using linear and ANN methods,"
13005, University Library of Munich, Germany.
- Gary Madden & Joachim Tan, 2008. "Forecasting international bandwidth capacity using linear and ANN methods," Applied Economics, Taylor & Francis Journals, vol. 40(14), pages 1775-1787.
- Yair Eilat & Liran Einav, 2004. "Determinants of international tourism: a three-dimensional panel data analysis," Applied Economics, Taylor & Francis Journals, vol. 36(12), pages 1315-1327.
- Koon Nam Lee, 2011. "Forecasting long-haul tourism demand for Hong Kong using error correction models," Applied Economics, Taylor & Francis Journals, vol. 43(5), pages 527-549.
- Johansen, Soren, 1991. "Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models," Econometrica, Econometric Society, vol. 59(6), pages 1551-80, November.
- De Gooijer, Jan G. & Kumar, Kuldeep, 1992. "Some recent developments in non-linear time series modelling, testing, and forecasting," International Journal of Forecasting, Elsevier, vol. 8(2), pages 135-156, October.
- Jane Binner & Rakesh Bissoondeeal & Thomas Elger & Alicia Gazely & Andrew Mullineux, 2005. "A comparison of linear forecasting models and neural networks: an application to Euro inflation and Euro Divisia," Applied Economics, Taylor & Francis Journals, vol. 37(6), pages 665-680.
- Ali Choudhary & Adnan Haider, 2008.
"Neural Network Models for Inflation Forecasting: An Appraisal,"
School of Economics Discussion Papers
0808, School of Economics, University of Surrey.
- M. Ali Choudhary & Adnan Haider, 2012. "Neural network models for inflation forecasting: an appraisal," Applied Economics, Taylor & Francis Journals, vol. 44(20), pages 2631-2635, July.
- Granger, Clive W. J. & Terasvirta, Timo, 1993. "Modelling Non-Linear Economic Relationships," OUP Catalogue, Oxford University Press, number 9780198773207, March.
- Nikolaos Vlastakis & George Dotsis & Raphael Markellos, 2008. "Nonlinear modelling of European football scores using support vector machines," Applied Economics, Taylor & Francis Journals, vol. 40(1), pages 111-118.
- Claveria, Oscar & Torra, Salvador, 2014. "Forecasting tourism demand to Catalonia: Neural networks vs. time series models," Economic Modelling, Elsevier, vol. 36(C), pages 220-228.
- Zhang, G. Peter & Qi, Min, 2005. "Neural network forecasting for seasonal and trend time series," European Journal of Operational Research, Elsevier, vol. 160(2), pages 501-514, January.
- Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
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