A technical analysis approach to tourism demand forecasting
C. Petropoulos,
Konstantinos Nikolopoulos (),
A. Patelis and
V. Assimakopoulos
Applied Economics Letters, 2005, vol. 12, issue 6, 327-333
Abstract:
Tourism demand forecasts are of great economic value both for the public and private sector. Any information concerning the future evolution of tourism flows, is of great importance to hoteliers, tour operators and other industries concerned with tourism or transportation, in order to adjust their policy and corporate finance. In the last few decades, numerous researchers have studied international tourism demand and a wide range of the available forecasting techniques have been tested. Major focus has been given to econometric studies that involve the use of least squares regression to estimate the quantitative relationship between tourism demand and its determinants. However, econometric models usually fail to outperform simple time series extrapolative models. This article introduces a new approach to tourism demand forecasting via incorporating technical analysis techniques. The proposed model is evaluated versus a range of classic univariate time series methods in terms of forecasting and directional accuracy.
Date: 2005
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)
Downloads: (external link)
http://www.informaworld.com/openurl?genre=article& ... 40C6AD35DC6213A474B5 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:apeclt:v:12:y:2005:i:6:p:327-333
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RAEL20
DOI: 10.1080/13504850500065745
Access Statistics for this article
Applied Economics Letters is currently edited by Anita Phillips
More articles in Applied Economics Letters from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().