scholarly journals Analyzing the Influence of Transportations on Chinese Inbound Tourism: Markov Switching Penalized Regression Approaches

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 515
Author(s):  
Woraphon Yamaka ◽  
Xuefeng Zhang ◽  
Paravee Maneejuk
Keyword(s):  
Structural Changes ◽  
Markov Switching ◽  
Penalized Regression ◽  
Tourism Demand ◽  
Switching Regression ◽  
Inbound Tourism ◽  
Dimensional Parameters ◽  
Demand Equations ◽  
Transportation Development ◽  
Positive Effect

This study investigates the nonlinear impact of various modes of transportation (air, road, railway, and maritime) on the number of foreign visitors to China originating from major source countries. Our nonlinear tourism demand equations are determined through the Markov-switching regression (MSR) model, thereby, capturing the possible structural changes in Chinese tourism demand. Due to many variables and the limitations from the small number of observations confronted in this empirical study, we may face multicollinearity and endogeneity bias. Therefore, we introduce the two penalized maximum likelihoods, namely Ridge and Lasso, to estimate the high dimensional parameters in the MSR model. This investigation found the structural changes in all tourist arrival series with significant coefficient shifts in transportation variables. We observe that the coefficients are relatively more significant in regime 1 (low tourist arrival regime). The coefficients in regime 1 are all positive (except railway length in operation), while the estimated coefficients in regime 2 are positive in fewer numbers and weak. This study shows that, in the process of transportation, development and changing inbound tourism demand from ten countries, some variables with the originally strong positive effect will have a weak positive effect when tourist arrivals are classified in the high tourist arrival regime.

scholarly journals Positive Effect of Electroacupuncture Treatment on Gut Motility in Constipated Mice Is Related to Rebalancing the Gut Microbiota

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Mingmin Xu ◽  
Lu Wang ◽  
Yu Guo ◽  
Wei Zhang ◽  
Ying Chen ◽  
...  
Keyword(s):  
Gut Microbiota ◽  
Structural Changes ◽  
Functional Constipation ◽  
Therapeutic Modality ◽  
Functional Bowel Disorder ◽  
Intestinal Dysbiosis ◽  
The Individual ◽  
Positive Effect ◽  
Electroacupuncture Treatment

Functional constipation (FC) is a common and often recurrent functional bowel disorder that seriously affects the quality of life of affected individuals and incurs a significant economic burden on both the individual and society. There is accumulating evidence that intestinal dysbiosis contributes to constipation and that rebalancing the gut microbiota may be a novel therapeutic modality for FC. Electroacupuncture (EA) has been shown to restore the gut microbiota to normal levels in a variety of diseases. Additionally, several high-quality clinical studies have confirmed that EA is an effective, sustained, and safe treatment for FC. However, whether the effects of EA are secondary to changes in the gut microbiota and how EA modulates intestinal dysbiosis induced by constipation are unknown. Therefore, here, we focused on the potential regulatory mechanisms of EA on diphenoxylate-induced constipation in mice by analyzing structural changes in the gut microbiota. Our results showed that EA treatment effectively rebalanced the gut microbiota of constipated mice, mainly by decreasing the Firmicutes/Bacteroidetes ratio, which may represent one way in which EA promotes gastrointestinal motility and alleviates constipation. Our findings lay the foundation for further mechanistic and clinical research into the application of EA in patients with FC.

Forecasting Tourism Demand With a New Time-Varying Forecast Averaging Approach

2021 ◽  
pp. 004728752110612
Author(s):  
Yuying Sun ◽  
Jian Zhang ◽  
Xin Li ◽  
Shouyang Wang
Keyword(s):  
Structural Changes ◽  
Model Averaging ◽  
Structural Instability ◽  
Time Varying ◽  
Forecast Combination ◽  
Tourism Demand ◽  
Single Model ◽  
Forecasting Accuracy ◽  
Combination Methods ◽  
Out Of Sample

Existing research has shown that combination can effectively improve tourism forecasting accuracy compared with single model. However, the model uncertainty and structural instability in combination for out-of-sample tourism forecasting may influence the forecasting performance. This paper proposes a novel forecast combination approach based on time-varying jackknife model averaging (TVJMA), which can more efficiently handle structural changes and nonstationary trends in tourism data. Using Hong Kong tourism demand from five major tourism source regions as an empirical study, we investigate whether our proposed nonparametric TVJMA-based approach can improve tourism forecasting accuracy further. Empirical results show that the proposed TVJMA-based approach outperforms other competitors including single model and three combination methods in most cases. Findings indicate the outstanding performance of our method is robust to various forecasting horizons and different estimation periods.

Forecasting and modelling for the inbound tourism demand volatility

2021 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Choy Leong Yee ◽  
Yuhanis Abdul Aziz ◽  
Wei Chong Choo ◽  
Yuruixian Zhang ◽  
Jen Sim Ho
Keyword(s):  
Tourism Demand ◽  
Inbound Tourism ◽  
Demand Volatility

scholarly journals ANALISIS HUBUNGAN PENDAPATAN WISATAWAN DAN HARGA PARIWISATA TERHADAP PERMINTAAN PARIWISATA DENGAN VECM

2016 ◽  
Vol 5 (2) ◽  
pp. 44
Author(s):  
MERARY SIANIPAR ◽  
NI LUH PUTU SUCIPTAWATI ◽  
KOMANG DHARMAWAN
Keyword(s):  
Unit Root ◽  
Unit Root Test ◽  
Cointegration Test ◽  
Tourism Demand ◽  
Long Run ◽  
Negative Effect ◽  
Short Run ◽  
The Relationship ◽  
Positive Effect

Tourism demand is focused on estimating variables which influence tourist visit. The tourism demand that we discuss on this research is the tourism demand to Bali of the major tourism-generating country was Australia. The aim of this research is to analyze the relationship between tourist income and tourism price to tourism demand using VECM. VECM requires that the variables in the model must be stationary and fulfilled a cointegration condition. In order to make it valid, the stationarity of variables in the model have to be checked using ADF unit root test. In additon, cointegration between these variables are examined using Johansen’s cointegration test. The results of ADF unit root test show that indicated the tourist income, the tourism price and the tourism demand for Australia data are stationary in first lag or I(1). Cointegration test shows that all variables are cointegrated, i.e. have a long-run relationship. In the long-run, the tourist income and tourism price give positive effect to the tourism demand. This means, the increase of tourist income and tourism price will contribute to the increase in tourism demand. In addition, in the short-run, the tourist income and the tourism price give negative effect to the tourism demand. This means, the increase of tourist income and tourism price will contribute to the decrease in tourism demand.

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