Naslov
TOURISM DEMAND FORECASTING – THE CASE OF THE CITY VARAŽDIN

Autori
Rončević, Ante ; Đukec, Damira ; Primorac, Dinko

Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, prošireni sažetak, znanstveni

Izvornik
Materiali V. Internationalscientificand practical conference "Managementof the turism industry: methodology and practice" / - Poltava : Poltava national technical Yuri Kondratuyk University, UA, 2018, 4-7

ISBN
978-966-9749-49-9

Skup
5th International scientific and practical conference: Managementof the turism industry: methodology and practice

Mjesto i datum
Poltava, Ukrajina, 03.10.2018. - 04.10.2018

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
turisam, demand forcasting, City Varaždin

Sažetak
Tourism demand forecasting methods can be divided in qualitative and quantitative methods. For the purpose of this paper we will focus on quantitative methods. Quantitative methods used for forecasting purposes are either time series models or econometric studies. We will further explore the methodologies applied in both of these categories of models, discussing their strengths and weaknesses. Econometric models differ from time series in identifying the casual relationships between variables. Table 1. summarizes the most extensively used methodologies in tourism demand forecasting. Time series models mostly rely on Box and Jenkins autoregressive integrated moving average (ARIMA) or seasonal ARIMA (SARIMA) methodology. Generalised Autoregressive Conditional Heteroscedastic (GARCH) models are also used as an extension of univariate time series analysis. Econometric models use explanatory variables such as tourist income, tourism prices in a destination relative to origin country, tourism prices of competing destinations and exchange rates to model and predict tourism demand. Some of the techniques applied are regression analysis based on ordinary least squares (OLS), error correction models (ECM), vector autoregressive models (VAR), time varying parameter (TVP), structural equation modelling (SEM), autoregressive distributed lagged model (ADLM) and the almost ideal demand system (AIDS). Other than above mentioned methods, recent developments include empirical applications of artificial intelligence (AI) such as artificial neural network method (ANN), the fuzzy time series method and genetic algorithms (GAs).

Izvorni jezik
Engleski

Znanstvena područja
Ekonomija

POVEZANOST RADA