A Panel Unit Root and Panel Cointegration Test of the Modeling International Tourism Demand in India
Bibliographic record
Abstract
This paper sought to find the long-run relationships between international tourist arrivals in India with economic variables such as GDP, transportation costs and the exchange rate for the period from 2002-2006. The cointegration techniques used was based on Panel Cointegration Test as well as both the OLS estimator and DOLS estimator were used to find long-run relationship of the international tourism demand model for India. This paper used the five standard method test for Panel Unit Root Tests such as Levin, Lin and Chu (2002), Breitung (2000), Im, Pesaran and Shin (2003), Maddala and Wu (1999) and Choi (2001) and Handri (1999). The long-run results indicate that growth in income (GDP) of India’s major tourist source markets has a positive impact on international visitor arrivals to India. These empirical results imply that when GDP of international major tourist source market such as England, America, Canada, France, German, Japan, Malaysia, Australia, Singapore and Korea increasing 1% then the number of international visitor arrivals to India increasing about 3% to 4%. As well as when transportation cost of these country increasing 1% then the number of international visitor arrivals to India increasing about 0.3% to 0.6%. Finally when the value of India’s currency strong than the value of these country’s currency increasing 1% then the number of international visitor arrivals to India decreasing 0.003% to 0.006%. Furthermore mostly findings were consistent with economic theory and the implications of the model can be use for policy making
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".