Estimation of tourism demand function for Australian destinations: a time-varying parameter approach

Assadzadeh, Ahmad; Mamipour, Siab; Najafi, Mir Hojjat

doi:10.1080/13032917.2013.837397

Cited by 6 publications

(4 citation statements)

References 20 publications

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“…Similarly, Smeral and Song (2015) analyzed the total tourism imports at constant prices and exchange rates for four source markets (Australia, Canada, EU-15, and Japan) and demonstrated a significant time variation of income and price elasticities over three decades. Assadzadeh, Mamipou, and Najafi (2014) analyzed foreign tourism demand for Australia during the period 1976–2012. This study also applied the TVP approach, and the findings showed that the income elasticity, the price elasticity, and habit formation have a steady declining trend over the observation period.…”

Section: Reasons Why Tourism Demand Elasticities May Varymentioning

confidence: 99%

Tourism Forecasting Performance Considering the Instability of Demand Elasticities

Smeral

2016

Journal of Travel Research

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This study highlights that accounting for the fact that tourism elasticities do not remain stable is crucial for forecasting situations. We demonstrate that approaches with constant elasticity assumptions might lead to substantial forecasting failures, especially in periods characterized by major economic fluctuations and changes in the macroeconomic environment. Therefore, in the course of distinct business cycles, we have to take into account that different price and income effects are to be expected. The main reasons why income elasticity may vary across the business cycle include loss aversion, liquidity constraints, and precautionary savings. By analyzing smooth transition autoregressive models and time-varying parameter approaches, we demonstrate that elasticities may vary as a result of structural changes in consumer behavior and/or policy regime shifts. Income elasticities may also change in the medium term in line with the worsening of the macroeconomic environment and indicate that tourism is no longer a luxury good.

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Section: Reasons Why Tourism Demand Elasticities May Varymentioning

confidence: 99%

Tourism Forecasting Performance Considering the Instability of Demand Elasticities

Smeral

2016

Journal of Travel Research

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“…Likewise, ECM was adopted by Pokharel et al (2018) to determine short-term and long-term relationships of the number of international tourists visiting Nepal in the years between 1962 and 2012. Another causal model called the time-varying parameter (TVP) had gained some attention from scholars in the tourism forecasting literature, as can be seen in Jorge-Gonzalez et al (2019) and Assadzadeh and Mamipour (2013). Despite such attention, the TVP is not as prevalent as the other causal models in tourism demand forecasting (Panahi et al, 2015).…”

Section: Causal Modeling Approachesmentioning

confidence: 99%

Hybrid SVR-SARIMA model for tourism forecasting using PROMETHEE II as a selection methodology: a Philippine scenario

Abellana

Rivero

Aparente

et al. 2020

JTF

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Purpose This paper aims to propose a hybrid-forecasting model for long-term tourism demand forecasting. As such, it attempts to model the tourism demand in the Philippines, which is a relatively underrepresented area in the literature, despite its tourism sector’s growing economic progress. Design/methodology/approach A hybrid support vector regression (SVR) – seasonal autoregressive integrated moving averages (SARIMA) model is proposed to model the seasonal, linear and nonlinear components of the tourism demand in a destination country. The paper further proposes the use of multiple criteria decision-making (MCDM) approaches in selecting the best forecasting model among a set of considered models. As such, a preference ranking organization method for enrichment of evaluations (PROMETHEE) II is used to rank the considered forecasting models. Findings The proposed hybrid SVR-SARIMA model is the best performing model among a set of considered models in this paper using performance criteria that evaluate the errors of magnitude, directionality and trend change, of a forecasting model. Moreover, the use of the MCDM approach is found to be a relevant and prospective approach in selecting the best forecasting model among a set of models. Originality/value The novelty of this paper lies in several aspects. First, this paper pioneers the demonstration of the SVR-SARIMA model’s capability in forecasting long-term tourism demand. Second, this paper is the first to have proposed and demonstrated the use of an MCDM approach for performing model selection in forecasting. Finally, this paper is one of the very few papers to provide lenses on the current status of Philippine tourism demand.

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“…In 2019, 1.5 billion international tourist arrivals were recorded by the World Tourism Organization (UNWTO). This explains the importance of the tourism industry in the world economy (Assadzadeh et al, 2014). According to the World Tourism Travel Council (WTTC, 2021), Travel and Tourism (including its direct, indirect and induced impacts) accounted for one in four of all new jobs created across the world, 10.6% of all jobs (334 million) and 10.4% of global GDP (US$9.2 trillion) in 2019.…”

Section: Introductionmentioning

confidence: 99%

Analysing the dynamic co-movement between tourism and expected economic growth considering extreme events

et al. 2022

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In the post epidemic period, it is important to clarify the relationship between economic growth and tourism growth. The aim of this study is to characterize the co-movement between expected macroeconomic conditions and inbound tourism, focussing on the nonlinear relationship between these variables. Dynamic copula-based GJR-GARCH models are employed to measure nonlinear dependence. Total inbound tourism to Thailand and its disaggregation for its 10 main origin countries, are considered in the empirical study. The results show that there exists significant co-movement between expected economic growth and inbound tourism, and prove the importance of considering nonlinearities and extreme events. Also, copula nonlinear Granger causality is used to conclude that inbound tourism and expected economic growth have nonlinear bi-directional and unidirectional Granger causality relationship. Finally, this paper puts forward some policy suggestions to restore tourism and boost economic growth in the post epidemic period.

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Estimation of tourism demand function for Australian destinations: a time-varying parameter approach

Cited by 6 publications

References 20 publications

Tourism Forecasting Performance Considering the Instability of Demand Elasticities

Tourism Forecasting Performance Considering the Instability of Demand Elasticities

Hybrid SVR-SARIMA model for tourism forecasting using PROMETHEE II as a selection methodology: a Philippine scenario

Analysing the dynamic co-movement between tourism and expected economic growth considering extreme events

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