Determinants of International Travel between Canada and the United States*

Laber, Gene

doi:10.1111/j.1538-4632.1969.tb00628.x

Cited by 26 publications

(6 citation statements)

References 3 publications

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“…Laber (1969) found that distance between 'origin' and 'destination' plays a signifi cant role as a determinant of tourism demand. As our collected statistics only report the tourism arrivals from a region -Asia, Europe, the Americas or Oceania, thus we need to manipulate the average distance of a country from a region.…”

Section: Travelling Costmentioning

confidence: 99%

A study of the non‐economic determinants in tourism demand

Cho

2010

Journal of Tourism Research

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Traditionally, many studies have attempted to use economic demand models. This paper stresses on the infl uence of non-economic factors on tourism demand. Some researchers have suggested that tourists from different origins have various cultural and nationalistic backgrounds, and they may interpret visual imagery and experiences differently. Aligning with this suggestion, we have investigated different underlying factors of tourism demand from four continents (Asia, the Americas, Europe and Oceania). Statistical data are collected from international organisations and 135 countries were covered. Our results showed that there are differences and similarities among the factors in determining the tourism demand. Copyright © 2009 John Wiley & Sons, Ltd. Received 18 November 2008; Revised 25 June 2009; Accepted 30 June 2009 Keywords: tourism demand; non-economic determinants; holistic approach. INTRODUCTION International tourism today has social, cultural and political signifi cance, as well as substantial economic benefi ts. In the last 50 years, tourism has emerged as one of the largest and fastest growing industries in the world (Eadington and Redman, 1991; WTO, 1992). According to the World Tourism Organization (WTO), the number of international tourists worldwide increased from 25 million in 1950 to 160 million in 1970, 429 million in 1990, 689 million in 2001, 846 million in 2006 and 1.6 billion by 2020. International tourism has experienced an overwhelming boom over the last two decades and has now been called the largest industry in the world. As a result of the rise in the number of tourists, and the importance of the tourism sector for many countries which have begun to channel their resources into its development (Balaguer and CantavellaJorda, 2002), tourism demand analysis has become increasingly important.In general, the international tourism demand model, which is based on classical economic theory, is typically estimated as a function of tourists' income, tourism prices in a destination relative to those in the origin country, tourism prices in the competing destinations (i.e. substitute prices), exchange rates, transportation cost between destination and origin, as well as dummy variables on various special events and deterministic trends (e.g. Barry and O'Hagan, 1972;Loeb, 1982;Stronge and Redman, 1982;Uysal and Crompton, 1984;Smeral, 1988;Di Matteo and Di Matteo, 1993;Crouch, 1994;Lim, 1999;Croes, 2000;Vanegas and Croes, 2000;Song et al., 2003;Chu, 2004;Li et al., 2005; Song and Witt, 2006;Wong et al., 2007;Chu, 2008;Song and Li, 2008). It postulates that factors of income and price are likely to play a central role in determining the demand for international tourism. As international tourism is generally regarded to be a luxury commodity or service, it is not surprising that the study of such variables has dominated past research.There are three reasons why the discussed economic framework needed to be extended. First, from the consumers' perspective, travelling overseas is one of the man...

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Section: Travelling Costmentioning

confidence: 99%

A study of the non‐economic determinants in tourism demand

Cho

2010

Journal of Tourism Research

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“…The economic literature on modelling and forecasting arrivals and number of tourists (national and international) is extensive. The literature of tourism modelling and forecasting began as early as the 1960s (see, for example, Gray, 1966 andLaber, 1969). For example, we find approaches based on error correction models and cointegration techniques (see for example Syriopoulos, 1995;Kulendran, 1996;Kulendran and King, 1997;Seddighi and Shearing, 1997;Kim andSong, 1998 andVogt andWittayakorn, 1998 among others), while other authors use pure time series analytical models (see García-Ferrer and Queralt, 1997;Chu, 1998;Kim, 1999, Lim and McAleer, 2001Goh and Law, 2002;Gustavsson andNordström, 2001 andBrännäs et al, 2002).…”

Section: Introductionmentioning

confidence: 90%

Modelling International Monthly Tourist in Spain*

Cuñado¹,

Gil-Alana²,

Gracia³

2020

EEA

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This article investigates the degree of persistence in the international monthly tourist time series in Spain using long memory (fractional integration) techniques. Our findings can be summarized as follows. The two standard hypotheses of integer degrees of differentiation, i.e., the I(0) and the I(1) behaviour, are clearly rejected. The series is found to be I(d) with a value of d in the interval (0.421, 0.780) thus implying long memory behaviour and mean reverting behaviour. However, if a structural break is considered, it takes place at May 2007, and then, the two subsamples present orders of integration which are above 1 and thus rejecting the mean reverting hypothesis.

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“…To analyze the determinants, econometric models have been applied in the forecasting of tourism demand. The most commonly used have been: the vector autoregressive model (Cao et al, 2017), the autoregressive distributed lag model (Song et al, 2003), the static regression models (Laber, 1969), the gravity model (Morley et al, 2014), the social benefit estimation models (Cesario and Knetsch, 1976), the spatial econometric interaction models (Marrocu and Paci, 2013), the state-space models (Jorge-González et al, 2020), the error correction models (Wong et al, 2007), the autoregressive distributed lag models (Li et al, 2011), the almost ideal demand system (Saayman et al, 2018), the time-varying parameter models (Song and Wong, 2003), and the structural equation models (Ko and Stewart, 2002).…”

Section: Literature Reviewmentioning

confidence: 99%

Collaborative forecasting of tourism demand for multiple tourist attractions with spatial dependence: A combined deep learning model

Han

Yao

2023

Tourism Economics

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To forecast the tourism demand across a set of tourist attractions with spatial dependence, a new model is proposed, which has three stages: tourist attraction selection, base predictor generation, and base predictor combination. In stage 1, a method for selecting associated attractions based on multi-dimensional scaling is used to determine the strength of the spatial dependence between each pair of attractions. In stage 2, a hybrid base predictor based on LSTM networks and Autoregressive model is developed, where the LSTM networks are used to capture the spatial dependence among attractions, and the Autoregressive model is used capture the scale of tourist volume at each attraction. In stage 3, a strategy for combining these base predictors is proposed; it can alleviate the overfitting problem of LSTM and improve the stability of forecasts. Finally, the superiority of the model is verified through the data on tourist volumes at 77 attractions in Beijing.

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Determinants of International Travel between Canada and the United States*

Cited by 26 publications

References 3 publications

A study of the non‐economic determinants in tourism demand

A study of the non‐economic determinants in tourism demand

Modelling International Monthly Tourist in Spain*

Collaborative forecasting of tourism demand for multiple tourist attractions with spatial dependence: A combined deep learning model

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