Modelling and forecasting the demand for Peruvian tourism

Pao Yue-kong Library Electronic Theses Database

Modelling and forecasting the demand for Peruvian tourism

 

Author: Roekaert, Edward
Title: Modelling and forecasting the demand for Peruvian tourism
Degree: DHTM
Year: 2014
Subject: Tourism -- Peru -- Forecasting.
Hong Kong Polytechnic University -- Dissertations
Department: School of Hotel and Tourism Management
Pages: xi, 188 leaves : illustrations (some color) ; 30 cm
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b2761690
URI: http://theses.lib.polyu.edu.hk/handle/200/7791
Abstract: The main objectives of this thesis are to identify the main factors that contribute to demand for tourism to Peru by utilizing econometric models, to generate forecasts of regional tourism arrivals to Peru for the period 2014 - 2020, and to determine demand elasticities in order to provide government officials and industry executives the possibility to assess the direction and magnitude in which tourist would respond to given changes in the determining factors in the forecasting models. Based on a review of published studies on tourism demand analysis, and given Peruvian tourism demand has not been modelled using econometric forecasting techniques, the autoregressive distributed lag model (ADLM) is used to capture the dynamics of economic activities as they are related to tourism demand to Peru by residents from the 17 major origin countries that represent at least 90% of total demand. The empirical results reveal that the most important factors that determine demand for Peruvian tourism are the costs of tourism in Peru, the income level in the origin countries, the costs of tourism in substitute destinations and the lag variables representing a ‘word of mouth’ effect. Finally, the forecasts of tourism arrivals and the demand elasticities obtained from the models will form the basis of policy formulations for the Peruvian government and the tourism industry, contributing to better and more effective decision making processes that will help sustain the growth and competitiveness of the tourism industry in Peru.

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