Forecasting tourist arrivals using time-varying parameter structural time series models

Song, Haiyan; Li, Gang; Witt, Stephen F.; Athanasopoulos, George

doi:10.1016/j.ijforecast.2010.06.001

Cited by 124 publications

(103 citation statements)

References 25 publications

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“…Athanasopoulos and Hyndman (2008) found that combining the ISS model with exogenous variables captures time series dynamics well and outperforms the regression models. Song et al (2011a) Guizzardi and Stacchini (2015) incorporated business sentiment indicators in naïve and STS models and noted that forecasting accuracy was improved.…”

Section: Non-causal Time Series Methodsmentioning

confidence: 99%

“…The TVP technique has been applied to tourism demand analysis (e.g. Page et al, 2012) and in conjunction with other advanced econometric techniques to develop more sophisticated models such as the TVP-STS (Song et al, 2011a) (Wu et al, 2012a).…”

Section: Econometric Methodsmentioning

confidence: 99%

“…Hence, some recent studies treat seasonality as stochastic by identifying and eliminating the seasonal unit roots before building a model or decomposing the demand series into a few unobserved components, including the seasonal component, and then specify them in a structure time series model (e.g. Guizzardi and Stacchini, 2015;Song et al, 2011a). Although more appropriate treatments of seasonality tend to improve the accuracy of seasonal tourism and hotel demand forecasting, business needs cannot be fully satisfied by quarterly or monthly predictions, given the increasingly dynamic nature of the demand system and the growing trend of late booking.…”

Section: Data Frequencymentioning

confidence: 99%

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New developments in tourism and hotel demand modeling and forecasting

Song

Shen

2017

IJCHM

Self Cite

244

217

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Design/Methodology/approachArticles on tourism and hotel demand modeling and forecasting published in both Science Citation Index (SCI) and Social Sciences Citation Index (SSCI) journals were identified and analyzed. FindingsThis review finds that the studies focused on hotel demand are relatively less than those on tourism demand. It is also observed that more and more studies have moved away from the aggregate tourism demand analysis, while disaggregate markets and niche products have attracted increasing attention. Some studies have gone beyond neoclassical economic theory to seek additional explanations of the dynamics of tourism and hotel demand, such as environmental factors, tourist online behavior, and consumer confidence indicators, among others. More sophisticated techniques such as Research limitations/implicationsThe main limitation of this review is that the articles included in this study only cover the English literature. Future review of this kind should also include articles published in other languages. The review provides a useful guide for researchers who are interested in future research on tourism and hotel demand modeling and forecasting. Practical implicationsThis review provides important suggestions and recommendations for improving the efficiency of tourism and hospitality management practices. Originality/valueThe value of this review is that it identifies the current trends in tourism and hotel demand modeling and forecasting research and points out future research directions.

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Section: Non-causal Time Series Methodsmentioning

confidence: 99%

Section: Econometric Methodsmentioning

confidence: 99%

Section: Data Frequencymentioning

confidence: 99%

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New developments in tourism and hotel demand modeling and forecasting

Song

Shen

2017

IJCHM

Self Cite

244

217

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“…For instance, Metzgen-Quemarez (1990) used real GDP figures from the United States, amongst other factors ;Var et al (1990) and Icoz et al (1998) employed Turkish Consumer Price Index (CPI) figures and the Turkish Lira currency exchange rate against the currency units from the tourists' country of origin, respectively; Greenidge (2001), used real GDP and CPI of the country of origin as well as the price index of tourism in Barbados and finally, Song et al (2010) employed GDP data of the country of origin and CPI in Hong Kong relative to the country of origin adjusted by the exchange rate. This paper seeks to break new ground by analyzing, for first time in the literature, the impact of macroeconomic shocks from the country of origin on future short term tourism demand to Greece.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Tourist Arrivals in Greece and the Impact of Macroeconomic Shocks from the Countries of Tourists’ Origin

Gounopoulos

Petmezas

Santamaría

2012

Annals of Tourism Research

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This paper generates short-term forecasts on tourist arrivals in Greece and performs impulse response analysis to measure the impact of macroeconomic shocks from the origin country on future tourism demand. We find the ARIMA (1, 1, 1) model outperforms exponential smoothing models in forecasting the direction of one year out of sample forecasts. However, this does not translate into point forecasting accuracy. Impulse response analysis on the impact of unemployment and tourists' cost of living shocks shows that the source of downside risk to future tourism numbers is limited in scope, magnitude, and duration. Shocks to consumer confidence from the origin countries have no impact on future tourism demand. Our results offer important insights and implications for policymakers and tourist operators.

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“…According to Songa et al (2011), seasonality affects tourism in various different ways and is responsible for difficulties in gaining access to capital, high risks of investment and business failures, the ineffective utilisation of resources and facilities, and difficulties in maintaining a consistent service quality. Seasonality can be defined as "the temporal imbalance in the phenomenon of tourism, which may be expressed in terms of dimensions of such elements as numbers of visitors, expenditure of visitors, traffic on highways and other forms of transportation, employment and admissions to attractions", (Butler, 1994;Cuccia, Rizzo, 2011).…”

Section: Literature Reviewmentioning

confidence: 99%