Research Note: Tourism Demand Forecasting with SARIMA Models – the Case of South Tyrol-Reference-Cited by-同舟云学术

Research Note: Tourism Demand Forecasting with SARIMA Models – the Case of South Tyrol

Published:2011-02 Issue:1 Volume:17 Page:209-221
ISSN:1354-8166
Container-title:Tourism Economics
language:en
Short-container-title:Tourism Economics

Author:

Brida Juan Gabriel¹,Risso Wiston Adrián²

Affiliation:

1. Competence Centre in Tourism Management and Tourism Economics (TOMTE), School of Economics and Management, Free University of Bolzano, Piazzetta dell'Università, 39031 Brunico (BZ), Italy

2. School of Economics and Management, Free University of Bolzano, Italy

Abstract

In this study, the performance of SARIMA models is explored in the context of tourism demand forecasting by using monthly time series of the overnight stays in South Tyrol (Italy) from January 1950 to December 2005. The forecasting performance is assessed using data for January 2006 to December 2008, and the authors find evidence that the SARIMA(2,1,2)(0,1,1)–ARCH(1) outperform the alternative models.

Publisher

SAGE Publications

Subject

Tourism, Leisure and Hospitality Management,Geography, Planning and Development

Link

http://journals.sagepub.com/doi/pdf/10.5367/te.2011.0030

Reference23 articles.

1. Modelling and forecasting tourism from East Asia to Thailand under temporal and spatial aggregation

2. A Study on the Impact of SARS on the Forecast of Visitor Arrivals to China

3. A fractionally integrated autoregressive moving average approach to forecasting tourism demand

4. Analyzing and forecasting tourism demand with ARAR algorithm

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