A Neural Network model of the impact of political instability on tourism

Christo Panchev, Antonis Theocharous

Research output: Chapter in Book/Report/Conference proceedingConference proceedingpeer-review

Abstract

This paper presents an empirical integration of the dimensions of political instability with traditional exogenous variables, which are usually employed in econometric tourism demand forecasting, within a tourism demand model in order to investigate causal relationships between political instability and tourism. The work uses the POLINST Database, which contains events of political instability from 1977 to 1997 that took place in the Middle East - Mediterranean region. The model is based on a Focused Tapped Delay Line Neural Network (FTDNN) with a sliding time window of 12 months. The evaluation results show that our model can be used to achieve a good estimation of the effects of political instability on tourism. In an extended set of experiments we were able to show the relative importance of the political instability factors on tourism. Finally, our model also allowed to estimated the time lag between a political instability/terrorist event and the reduction of tourist number to the destination.
Original languageEnglish
Title of host publicationThe 2013 International Joint Conference on Neural Networks (IJCNN)
PublisherIEEE
Pages1-7
Number of pages7
ISBN (Print)978-1-4673-6129-3
DOIs
Publication statusPublished - 2013
Externally publishedYes
EventThe 2013 International Joint Conference on Neural Networks - Dallas, United States
Duration: 4 Aug 20139 Aug 2013

Publication series

NameProceedings of International Joint Conference on Neural Networks
ISSN (Print)2161-4407

Conference

ConferenceThe 2013 International Joint Conference on Neural Networks
Abbreviated titleIJCNN
CountryUnited States
CityDallas
Period4/08/139/08/13

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