Modeling and Forecasting Chinese Outbound Tourism: An Econometric Approach

Lin, Vera Shanshan; Liu, Anyu; Song, Haiyan

doi:10.1080/10548408.2014.986011

Cited by 76 publications

(66 citation statements)

References 33 publications

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“…We use M2 instead of M1 because the former reflects overall economic conditions better. Our choice of macroeconomic indicators is generally consistent with previous studies focused on the macroeconomic impacts on tourism [34,49,50].…”

Section: Modelsupporting

confidence: 56%

The Macroeconomic Determinants of International Casino Travel: Evidence from South Korea’s Top Four Inbound Markets

Suh

Kim

2018

Sustainability

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This study investigates the macroeconomic determinants behind the number of foreign visitors to Korean casinos arriving from major source countries, namely Japan, Mainland China, Hong Kong SAR and Taiwan. Using monthly data from 2006 to 2016, we utilized a Structural Vector Autoregression (SVAR) model to investigate the dynamic effects of macroeconomic factors on the number of casino visitors. Estimation results supported the conclusion that visitors from Japan and Mainland China-who account for approximately three quarters of the total number of foreign casino visitors-were affected by macroeconomic or financial indicators such as changes in oil prices, exports and exchange rates (Japan), exports and short-term interest rates (Mainland China). In contrast, visitors from Hong Kong SAR did not seem to be influenced by any of these factors. We also found that the MERS (Middle East Respiratory Syndrome) outbreak in Korea in 2015 had a significant adverse effect on casino visitors from all regions. Implications and suggestions for future studies are provided along with the results of this study.

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Section: Modelsupporting

confidence: 56%

The Macroeconomic Determinants of International Casino Travel: Evidence from South Korea’s Top Four Inbound Markets

Suh

Kim

2018

Sustainability

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“…China is predicted to become the fourth-largest generating source of international tourism with an estimation of 100 million outbound tourists by 2020 (UNWTO, 2001). UNWTO and the European Travel Commission (UNWTO & ETC, 2008) predict that this target number may be reached well before 2020 (Lin, Liu, & Song, 2015). Knowledge of how Chinese travelers make their destination choices is therefore of great interests to destinations interested in increasing their share of this segment.…”

Section: Introductionmentioning

confidence: 99%

Impact of cultural distance on international destination choices: The case of Chinese outbound travelers

Lehto

2017

Journal of Tourism Research

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Given the rapid growth of China outbound market, research pertaining to the specificities of this market bears significance to destination marketers and academicians. The focus of this research is on the impact of cultural distance on outbound tourist destination choices with Chinese travelers as the case in point. Based on outbound tourism statistics of China over the 1995-2014 periods, this research has developed an inverted U-curve model to account for the relationship between cultural distance and tourists' destination choice. Results show that the optimal cultural distance for Chinese outbound tourists' destination choice is 2.8757. Through evaluating the positions, potential destinations are located relative to the optimal cultural distance. The preference tourists place on potential destinations can be measured. This research bears valuable theoretical contributions and implications for destination marketing practices.

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“…where x i is input value; y i is the output value after BN; m is the size of the mini-batch, that is, a mini-batch with m inputs; µ B is the average of all inputs in the same mini-batch; σ 2 B is the variance of all inputs in the same mini-batch; next, obtaining the normalizedx i according to µ B , σ 2 B ,x i , and formula (12), puttingx i into formula (13), and obtaining y i ; γ and β are obtained through machine learning. Using BN can maximize the neurons in deep neural networks to improve training efficiency.…”

Section: Model Buildingmentioning

confidence: 99%

A Method Based on GA-CNN-LSTM for Daily Tourist Flow Prediction at Scenic Spots

Rui

Liang

et al. 2020

Entropy

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Accurate tourist flow prediction is key to ensuring the normal operation of popular scenic spots. However, one single model cannot effectively grasp the characteristics of the data and make accurate predictions because of the strong nonlinear characteristics of daily tourist flow data. Accordingly, this study predicts daily tourist flow in Huangshan Scenic Spot in China. A prediction method (GA-CNN-LSTM) which combines convolutional neural network (CNN) and long-short-term memory network (LSTM) and optimized by genetic algorithm (GA) is established. First, network search data, meteorological data, and other data are constructed into continuous feature maps. Then, feature vectors are extracted by convolutional neural network (CNN). Finally, the feature vectors are input into long-short-term memory network (LSTM) in time series for prediction. Moreover, GA is used to scientifically select the number of neurons in the CNN-LSTM model. Data is preprocessed and normalized before prediction. The accuracy of GA-CNN-LSTM is evaluated using mean absolute percentage error (MAPE), mean absolute error (MAE), Pearson correlation coefficient and index of agreement (IA). For a fair comparison, GA-CNN-LSTM model is compared with CNN-LSTM, LSTM, CNN and the back propagation neural network (BP). The experimental results show that GA-CNN-LSTM model is approximately 8.22% higher than CNN-LSTM on the performance of MAPE.

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Modeling and Forecasting Chinese Outbound Tourism: An Econometric Approach

Cited by 76 publications

References 33 publications

The Macroeconomic Determinants of International Casino Travel: Evidence from South Korea’s Top Four Inbound Markets

The Macroeconomic Determinants of International Casino Travel: Evidence from South Korea’s Top Four Inbound Markets

Impact of cultural distance on international destination choices: The case of Chinese outbound travelers

A Method Based on GA-CNN-LSTM for Daily Tourist Flow Prediction at Scenic Spots

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