Thailand's International Tourism Demand: Seasonal Panel Unit Roots and the Related Cointegration Model
Bibliographic record
Abstract
Tourism is the main service sector in Thailand. It generated about 6.5% of national income (GDP) in 2009. 547 billion baht came from international tourists and 380 billion baht from domestic tourists in 2008. This study analyzes panel data by using the seasonal unit roots test. Firstly, we apply the CHEGY-IPS panel seasonal unit roots test developed by Otero (2007). Secondly, we develop a long run relationship model to estimate the number of international tourists to Thailand from 1997 to 2010 using the generalized method of moments (GMM).The results reveal panel seasonal unit roots in all model variables: GDP of the tourists' country of origin, competitive ratio of CPI between Thailand and country of origin, currency exchange rates, and transportation costs. The results from cointegration estimation by the GMM demonstrate that there is a positive relationship between GDP and the number of international tourist arrivals: a 1% increase of GDP leading to an increase of 1.5% of the number of international tourists. Regarding the exchange rate, a negative relationship is found: a 1% stronger Thai currency will lead to a decrease of 0.55% in the number of international tourists. Lastly, season has a significant effect upon the number of tourists. The number of tourists in January to March is higher than in other quarters.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".