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Academic literature on the topic 'Tourism Victoria Econometric models'

Author: Grafiati
Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Tourism Victoria Econometric models"

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Witt, Stephen F., and Christine A. Martin. "Econometric Models for Forecasting International Tourism Demand." Journal of Travel Research 25, no. 3 (January 1987): 23–30. http://dx.doi.org/10.1177/004728758702500306.

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Тураев and B. Turaev. "Econometric Models for Tourism Management System Improvement." Economics 2, no. 6 (December 17, 2014): 40–46. http://dx.doi.org/10.12737/6733.

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Role and value of tourism as a factor for impact on economy are considered in this paper. Prerequisites and conditions promoting its development have been revealed. The need in development and implementation of data (including forecast data) in the frame of national economy complex evolution has been justified. The main dynamic and structural tendencies of tourism regional market development, using Uzbekistan as an example, with elements of comparative analysis related to similar indicators of tourism development at the level of world, national and regional economy are investigated in this paper as well. Proceeding from this analysis, recommendations on the tourist services market development have been given.
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Song, Haiyan, Stephen F. Witt, and Thomas C. Jensen. "Tourism forecasting: accuracy of alternative econometric models." International Journal of Forecasting 19, no. 1 (January 2003): 123–41. http://dx.doi.org/10.1016/s0169-2070(01)00134-0.

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Lim, Christine. "An Econometric Classification and Review of International Tourism Demand Models." Tourism Economics 3, no. 1 (March 1997): 69–81. http://dx.doi.org/10.1177/135481669700300105.

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The purpose of the paper is to provide an econometric classification and evaluation of 100 published empirical studies on modelling international tourism demand, according to the recognition and type of omitted explanatory variables, number and type of proxy variables used, method of estimation, and use of various diagnostic tests of the auxiliary assumptions of the various models. An analysis of the adequacy of model specifications and the statistical deficiencies of existing empirical tourism demand models will permit a greater appreciation of the factors which determine changes in international tourism demand and will aid in forecasting future tourism demand.
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Jiao, Eden Xiaoying, and Jason Li Chen. "Tourism forecasting: A review of methodological developments over the last decade." Tourism Economics 25, no. 3 (November 26, 2018): 469–92. http://dx.doi.org/10.1177/1354816618812588.

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This study reviewed 72 studies in tourism demand forecasting during the period from 2008 to 2017. Forecasting models are reviewed in three categories: econometric, time series and artificial intelligence (AI) models. Econometric and time series models that have already been widely used before 2007 remained their popularity and were more often used as benchmark models for forecasting performance evaluation and comparison with respect to new models. AI models are rapidly developed in the past decade and hybrid AI models are becoming a new trend. And some new trends with regard to the three categories of models have been identified, including mixed frequency, spatial regression and combination and hybrid models. Different combination components and combination techniques have been discussed. Results in different studies proved superiority of combination forecasts over average single forecasts performance.
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Abdou, Musonera, Edouard Musabanganji, and Herman Musahara. "Tourism Demand Modelling and Forecasting: A Review of Literature." African Journal of Hospitality, Tourism and Leisure 10(4), no. 10(4) (August 31, 2021): 1370–93. http://dx.doi.org/10.46222/ajhtl.19770720.168.

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This research examines 145 key papers from 1979 to 2020 in order to gain a better sense of how tourism demand forecasting techniques have changed over time. The three types of forecasting models are econometric, time series, and artificial intelligence (AI) models. Econometric and time series models that were already popular in 2005 maintained their popularity, and were increasingly used as benchmark models for forecasting performance assessment and comparison with new models. In the last decade, AI models have advanced at an incredible rate, with hybrid AI models emerging as a new trend. In addition, some new developments in the three categories of models, such as mixed frequency, spatial regression, and combination and hybrid models have been introduced. The main conclusions drawn from historical comparisons forecasting methods are that forecasting models have become more diverse, that these models have been merged, and that forecasting accuracy has improved. Given the complexities of predicting tourism demand, there is no single approach that works well in all circumstances, and forecasting techniques are still evolving.
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Baltas, George. "Econometric Models for Discrete Choice Analysis of Travel and Tourism Demand." Journal of Travel & Tourism Marketing 21, no. 4 (August 15, 2007): 25–40. http://dx.doi.org/10.1300/j073v21n04_04.

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Gričar, Sergej. "IMPLEMENTATION OF VECTOR AUTO-REGRESSION MODELS IN TOURISM: STATE OF THE ART ANALYSIS AND FURTHER DEVELOPMENT." Tourism and hospitality management 28, no. 3 (December 2022): 707–9. http://dx.doi.org/10.20867/thm.28.3.16.

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Purpose The dissertation focuses on time series analysis and is based on several research strategies and methods. The methodology used in the research process was published in four papers as part of international scientific journals indexed in the Web of Science database. Since tourism is one of the most lagged industries in science there is need for new and innovative approaches in key tourist sector determinants modelling and forecasting. This doctoral thesis introduces an extension of time series methodology that focuses on investigating and testing the normal distribution of residuals, as a key adequacy prerequisite of econometric models. This issue has not systematically been considered in quantitative approaches in tourism. The motivation for research of the doctoral thesis are multidimensional: to filter previous research on time series in tourism and to theoretically and empirically improve and redesign time series methodology and methods for tourism. Both issues were successfully presented in one of the published papers. Finally, tourism forecasts should be based on reliable models as evident, from the most recent shocks, ex-ante tourism forecasting has to be considered crucial in evaluating model efficiency. The dissertation aimed to research and develop appropriate econometric models able to capture the specifics of multiple interactions in the tourism market. The research seeks to develop econometric models for the Republics of Slovenia and Croatia, two countries whose economic development is predicated on tourism. Four goals and four specific objectives have been specified during the research process: 1) To introduce an improved time series approach in cointegrated panels. The first specific objective (SO1) is to test at least ten econometric modelling structures that reduce cycle breaks. 2) To examine previous theoretical thinking regarding the cointegration of time series, cross-sectional data, and panels. The second specific objective (SO2) is to outline at least 250 previous empirical studies for the tourism industry. 3) To examine cointegration in tourism data for Slovenia and Croatia. The third objective (SO3) is to model at least three econometric time series equations and mathematical theorems/ lemmas for the tourism industry. 4) To improve and better understand unit root tests in tourism. The specific objective (SO4) is to approach the design of at least three stable and innovative models. Methodology The research relies upon econometric modelling in time series and panels as well as misspecification tests implementation. The study is primarily oriented to the hypotheses testing on a reliable modelling procedure. The research methodology is based on time series and the vector autoregression model (VAR) implementation. Moreover, the cointegrated VAR and the error correction model (ECM) are used. The Granger causality is used to identify trends to determine the direction of the hypothesised research problems. Overall, the study uses regression analysis and summary descriptive statistics. The sensitive analysis relies on panel regression. Summarizing, the added value of the doctoral thesis can be reflected in investigating the normal distribution of time series residuals to obtain accurate results for interpretation and prediction. Findings The most significant research results include time series and panel testing and modelling based on research hypotheses. The main hypothesis (an innovative approach to cointegration, based on empirical evidence for Slovenia and Croatia, which provides unbiased, accurate and validated results for tourism development) was confirmed. The first published paper investigates the possibility and accuracy of using time series data in forecasting tourism demands. The theoretical added value provides ex-ante research results regarding the consequences of the most recent pandemic. The empirical part of the paper discusses the direction of daily Slovenian and Croatian COVID-19 infections and tourist arrivals. Hypothesis 1 the tourism industry in Slovenia has developed rapidly and is expected to continue growing in a positive and sustainable direction without seasonal fluctuation, and 2, the tourism industry in Croatia has a long tradition and opportunity to grow at unprecedented rates hitherto. Volatility in the Croatian tourism industry is significant and has a high standard deviation; were confirmed. Additionally, the modelling strategy was introduced in one of the published papers. The results emphasized a significant influence on tourism demand and, depending on the modelling methodology, the existence of an impact on tourist arrivals of chosen determinants. Moreover, two published papers discussed the direction of economic impacts on tourist arrivals and vice versa. The decisive significance of productivity to real gross wages with a rise in tourist arrivals was confirmed. Furthermore, prices in tourism based on short-run effects and two cointegrated relations were modelled and forecasted. It can be concluded that tourism demand, approximated by tourist arrivals, is volatile on different determinants which were previously not researched or tested by reliable econometrics. Therefore, the set goals and specific study objectives were achieved.
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Saleh, Mohammad H., Adnan Turki Garaibeh, Ali Shehadeh, and Jamil J. Jaber. "The Role of Tourism Activity in Economic Growth by Using Some Econometric Models Evidence from Jordan." Modern Applied Science 13, no. 6 (May 23, 2019): 1. http://dx.doi.org/10.5539/mas.v13n6p1.

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Tourism is used as a vital strategy to reach greater economic productivity. The aim of this paper is to investigate the dynamic relationship between touruism growth and economic growth, using annual time series data from Jordan over the period (1990-2016). The study used a variety of methods based on the cobb-Douglus production function, the Koyck geometrical lag model, cointegration analysis, error correction model and causality analysis. We reveal if tourism leads to economic growth or growth leads to tourism development or bidirectional relation exists between two variables. According to the results, there are moderately increasing return to scale in tourism sector. In addition to there is a positive long-term equilibrium relation related to growth with both fixed assets and tourism balance. The results of the Granger causality test have demonstrated the unidirectional hypothesis, where tourism development leads to economic growth. This paper presented a set of suggestions that would improve Jordan's competitiveness in tourism. 
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Liu, Han, Ying Liu, Yonglian Wang, and Changchun Pan. "Hot topics and emerging trends in tourism forecasting research: A scientometric review." Tourism Economics 25, no. 3 (November 4, 2018): 448–68. http://dx.doi.org/10.1177/1354816618810564.

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Tourism forecasting has been a focal point of tourism research over the past few decades as a result of the corresponding rapid development and expansion of the tourism industry. A bibliometric analysis, based on 543 articles retrieved from the Web of Science Core Collection database, was carried out to provide insights into hot topics as well as emerging trends in tourism forecasting research. The results show that the research outputs related to tourism forecasting have grown rapidly since 2006. The observed hot topics in tourism forecasting were to predict tourism demand via various models, including time series models, econometric models, and artificial intelligence-based methods, and to compare the forecasting accuracy of models. An emerging trend of tourism forecasting is to use methods based on data from a web-based search engine. Our study provides insights and valuable information for researchers to identify new perspectives on hot topics and research frontiers.
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Dissertations / Theses on the topic "Tourism Victoria Econometric models"

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Yap, Ghialy C. "An econometric analysis of Australian domestic tourism demand." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2010. https://ro.ecu.edu.au/theses/121.

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In 2007, the total spending by domestic visitors was AUD 43 billion, which was 1.5 times higher than the aggregate expenditure by international tourists in Australia. Moreover, domestic visitors consumed 73.7% of the Australian produced tourism goods and services whereas international tourists consumed 26.3%. Hence, this shows that domestic tourism is an important sector for the overall tourism industry in Australia. This present research determines the factors that influence domestic tourism demand in Australia and examines how changes in the economic environment in Australia could influence this demand. The main aim of this research is to achieve sustainability of domestic tourism businesses in Australia. In Chapters Two and Three, a review of the tourism demand literature is conducted. Most of the empirical papers argued that household income and travel prices are the main demand determinants. However, the literature has largely neglected other possible indicators, namely consumers‟ perceptions of the future economy, household debt and working hours, which may play an important role in influencing domestic tourism demand in Australia. The PhD thesis is divided into three parts. For the initial phase, a preliminary study is conducted using Johansen‟s cointegration analysis to examine the short- and long-run coefficients for the determinants of Australian domestic tourism demand. In the next section of this thesis, an alternative approach using panel data analysis to estimate the income and price elasticities of the demand is applied, as a panel data framework provides more information from the data and more degrees of freedom. In the final section, this thesis also investigates whether other factors (such as the consumer sentiment index, and measures of household debt and working hours) influence Australians‟ demand for domestic trips. This study reveals several distinct findings. First, the income elasticity for domestic visitors of friends and relatives (VFR) and interstate trips is negative, implying that Australian households will not choose to travel domestically when there is an increase in household income. In contrast, the study finds that the income variables are positively vi correlated with domestic business tourism demand, indicating that the demand is strongly responsive to changes in Australia‟s economic conditions. Second, an increase in the current prices of domestic travel can cause the demand for domestic trips to fall in the next one or two quarters ahead. Third, the coefficients for lagged dependent variables are negative, indicating perhaps, that trips are made on a periodic basis. Finally, to a certain extent, the consumer sentiment index, household debt and working hours have significant influences on domestic tourism demand. The current econometric analysis has significant implications for practitioners. A better understanding of income and travel cost impacts on Australian households‟ demand allows tourism companies to develop price strategies more effectively. Moreover, tourism researchers can use these indicators (such as measures of consumers‟ confidence about their future economy, household debt and working hours) to investigate how changes in these factors may have an impact on individual decisions to travel.
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Li, Gang. "Modelling and forecasting UK tourism demand in Western Europe : illustrations of TVP-LAIDS models' superiority over other econometric approaches." Thesis, University of Surrey, 2004. http://epubs.surrey.ac.uk/2100/.

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Altin, Mehmet. "Economic Sentiment Indicator as a Demand Determinant in Tourism: A Case of Turkey." Thesis, Virginia Tech, 2011. http://hdl.handle.net/10919/42577.

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Tourism is one of the fastest growing industries in the world, employing approximately 220 million people and generating over 9.4% of the world's GDP. The growing contribution of tourism is accompanied by an increased interest in understanding the major factors which influence visitation levels to those countries. Therefore, finding the right variables to understand and estimate tourism demand becomes very important and challenging in policy formulations. The purpose of this study is to introduce Economic Sentiment Indicator (ESI) to the field of tourism demand studies. Using ESI in demand analysis, this study will assist in the ability to tap into individuals' hopes and/or worries for the present and future. The study developed a demand model in which the number of tourist arrivals to Turkey from select EU countries is used as the dependent variable. ESI along with more traditional variables such as Interest Rate, Relative Price, and Relative Exchange Rate were brought into the model as the independent demand determinants. The study utilized such econometric models as ARIMA for seasonality adjustment and ARDL Bound test approach to cointegration for the long and short-run elasticities. ESI was statistically significant in 8 countries out of 13, three of those countries had a negative coefficient and five had a positive sign as proposed by the study. The study posits that ESI is a good indicator to gauge and monitor tourism demand and adding the visitors' state of mind into the demand equation could reduce errors and increase variance in arrivals. Policy makers should monitor ESI as it fluctuates over time. Since we do not have direct influence on travelers' demand for tourism, it is imperative that we use indirect approaches such as price adjustment and creating new packages or promotional expenditures in order to influence or induce demand. Using this information generated from the study, government officials and tourism suppliers could adjust their promotional activities and expenditures in origin countries accordingly.
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Brewbaker, Paul H. "Dynamic models of Hawaiʻi hotel investment." Thesis, 2004. http://proquest.umi.com/pqdweb?index=0&did=765924051&SrchMode=1&sid=2&Fmt=2&VInst=PROD&VType=PQD&RQT=309&VName=PQD&TS=1233272674&clientId=23440.

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Huo, Shuyan. "Modelling and forecasting international tourism demand to China." Thesis, 2002. https://vuir.vu.edu.au/15369/.

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China has experienced a massive growth in international tourism over the past two decades. To date, there have been few attempts to analyse this massive increase in the demand for international tourism to China. This study, therefore, employs modern econometric techniques to identify the important determinants of tourism demand to China and thus determine the best forecasting models of tourism demand applied to China. This thesis has set three objectives for itself. First, it aims to undertake the first application of modern time-series econometric techniques to modelling international tourism flows to China. Second, it provides the first application of the Vector Autoregression (VAR) approach to modelling the demand for tourism. Third, this thesis compares relative forecasting performance of the two econometric techniques — the time-series approach and the VAR approach, in order to provide 'best possible' forecasts of international tourism flows to China. The thesis models demand from three of China's most important markets for international travelers: Australia, the USA and Japan.
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Books on the topic "Tourism Victoria Econometric models"

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F, Witt Stephen, and Li Gang 1975-, eds. The advanced econometrics of tourism demand. New York: Routledge, 2009.

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F, Witt Stephen, ed. Tourism demand modelling and forecasting: Modern econometric approaches. Amsterdam: Pergamon, 2000.

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Kim, Chʻŏr-wŏn. A model development for measuring global competitiveness of the tourism industry in the Asia-Pacific region. Seoul, Korea: Korea Institute for International Economic Policy, 2000.

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Botswana, UNDP. Botswana: Strengthening tourism statistics and formulation of an experimental TSA--2005/2006. Gaborone: UNDP, 2007.

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service), ScienceDirect (Online, ed. Quantitative tourism industry analysis: Introduction to input-output, social accounting matrix modeling and tourism satellite accounts. Amsterdam: Butterworth-Heinemann, 2008.

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Tourism Demand Modelling and Forecasting (Advances in Tourism Research). Pergamon, 2000.

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Li, Gang, Haiyan Song, and Stephen F. Witt. Advanced Econometrics of Tourism Demand. Taylor & Francis Group, 2012.

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Li, Gang, Haiyan Song, and Stephen F. Witt. Advanced Econometrics of Tourism Demand. Taylor & Francis Group, 2008.

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Li, Gang, Haiyan Song, and Stephen F. Witt. Advanced Econometrics of Tourism Demand. Taylor & Francis Group, 2008.

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Li, Gang, Haiyan Song, and Stephen F. Witt. Advanced Econometrics of Tourism Demand. Taylor & Francis Group, 2008.

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Book chapters on the topic "Tourism Victoria Econometric models"

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Jiao, Xiaoying, and Jason Li Chen. "Spatiotemporal econometric models." In Econometric Modelling and Forecasting of Tourism Demand, 126–43. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003269366-6.

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Song, Haiyan, Egon Smeral, Gang Li, and Jason L. Chen. "21. Tourism Forecasting Using Econometric Models." In Trends in European Tourism Planning and Organisation, edited by Carlos Costa, Emese Panyik, and Dimitrios Buhalis, 289–310. Bristol, Blue Ridge Summit: Multilingual Matters, 2013. http://dx.doi.org/10.21832/9781845414122-025.

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Hu, Mingming, Mei Li, and Xin Zhao. "Hybrid forecasting models." In Econometric Modelling and Forecasting of Tourism Demand, 173–200. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003269366-8.

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Liu, Han, Ying Liu, and Peihuang Wu. "Mixed-frequency models." In Econometric Modelling and Forecasting of Tourism Demand, 144–72. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003269366-7.

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Cao, Zheng Chris. "Vector autoregressive models." In Econometric Modelling and Forecasting of Tourism Demand, 95–125. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003269366-5.

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FRECHTLING, D. "Causal methods: structural econometric models." In Forecasting Tourism Demand, 201–9. Elsevier, 2001. http://dx.doi.org/10.1016/b978-0-7506-5170-7.50012-x.

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"Causal methods: structural econometric models." In Forecasting Tourism Demand, 223–31. Routledge, 2012. http://dx.doi.org/10.4324/9780080494968-16.

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LI, G. "Forecasting Tourism Demand Using Econometric Models." In Tourism Management Dynamics, 219–28. Elsevier, 2006. http://dx.doi.org/10.1016/b978-0-7506-6378-6.50033-0.

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Getz, Donald. "Economic Impact Assessment." In Event Impact Assessment. Goodfellow Publishers, 2018. http://dx.doi.org/10.23912/978-1-911635-03-1-4028.

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Concepts for understanding economic impacts, and valid methods of assessment are well developed. In fact, there is so much information available that this is the largest chapter in the book – not the most important. A thorough and accessible reference on the subject is the book Tourism Economics and Policy by Dwyer, Forsyth and Dwyer (2010) as it contains a full chapter on events. Event Tourism (Getz, 2013) also covers economic impact assessment in detail. There have been well-documented problems with economic impact assessments for tourism and events (Matheson, 2002; Matheson and Baade, 2003; Crompton and McKay, 2004; Tyrell & Ismail, 2005; Crompton, 2006; Davies et al., 2013), pertaining to both how they are done and the purposes they serve. Dwyer and Jago (2014, p.130) identified three main types of criticisms associated with the assessment of the economic impacts of events, commencing with the exaggeration of benefits owing to either deliberate manipulation or faulty methods. Attention has often focused on the use of Input-Output tables to formulate ‘multipliers’, a practice which leads to exaggerated benefits, with a number of scholars preferring Computable General Equilibrium (CGE) modelling. Most fundamental is the frequent failure to consider all costs and benefits, leading to calls for more comprehensive cost-benefit analysis (CBA). Most economic IAs have utilized only a narrow range of metrics, but even more unfortunate is the continued reliance on multipliers and econometric models, as these ‘black-box’ approaches tend to exaggerate imputed benefits while ignoring costs and equity issues. This is certainly not in keeping with principles of social responsibility and sustainability.
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Conference papers on the topic "Tourism Victoria Econometric models"

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Folgieri, Raffaella, Tea Baldigara, and Maja Mamula. "ARTIFICIAL NEURAL NETWORKS-BASED ECONOMETRIC MODELS FOR TOURISM DEMAND FORECASTING." In Tourism in Southern and Eastern Europe 2017: Tourism and Creative Industries: Trends and Challenges. University of Rijeka, Faculty of Tourism and Hospitality Management, 2017. http://dx.doi.org/10.20867/tosee.04.10.

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Ivandić, Neven. "DOES TOURISM ACTIVITY AFFECT MIGRATION? EMPIRICAL EVIDENCE FROM CROATIA." In Tourism in Southern and Eastern Europe 2021: ToSEE – Smart, Experience, Excellence & ToFEEL – Feelings, Excitement, Education, Leisure. University of Rijeka, Faculty of Tourism and Hospitality Management, 2021. http://dx.doi.org/10.20867/tosee.06.24.

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Purpose – Since the Census in 2011, Croatia has seen a substantial population decline. Given the high contribution of tourism to the Croatian economy, this paper empirically analyses the relationship between population migration, namely total net migration and net migration abroad, and tourism activity. Methodology – The research design of this paper is based on quantitative econometric panel data analysis using annual data for cities and municipalities in Croatia between 2002 and 2019. A oneway and two-way fixed effects model are used for the estimation of the regression model coefficients. Findings – The set models provide insight into the relationship between net migration or migration abroad and tourism activity. Tourism can be seen as a generator of demographic change, especially in rural and less developed areas, as it generates employment opportunities and, thus, the opportunity for permanent residency. Contribution – The main contribution of this paper is the novel use of such detailed data at the geographical level that spans over two decades. This generates empirical insights that hold high levels of external validity. A further important aspect of the paper is the analysis of the connection between population migration and tourism activity in the context of Croatia's accession to the European Union and verification of the theoretically grounded expectation that tourism activity as pull factor is positively related to population net migration.
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