An ARDL Approach: How Robust Is Guatemala's International Tourism Demand?
Bibliographic record
Abstract
An autoregressive distributed lag (ARDL) bounds testing to cointegration was used to test the robustness of Guatemala's tourism demand from Canada, Costa Rica, El Salvador, Honduras, Mexico, Nicaragua, Panama, and the US. A robustness check was conducted on income, price, and travel cost variables. The magnitudes of the estimated income elasticity values differ from 1.41 (Panama) to 4.86 (Nicaragua). It is a greater luxury for Canada, Costa Rica, Mexico, Nicaragua, and the US than tourists from El Salvador, Honduras, and Panama. In the long run, a 1% steady growth in income in Canada and El Salvador would lead to an increase in tourist arrivals by 4.33% and 3.28%, respectively, ceteris paribus. Similar results, except for El Salvador and Panama, were found for the price and the cost of travel variables. This findings on the price and cost variables imply that its statistical significance does not depend on the measures used. The results are robust to the inclusion of a composite price or separated price, and exchange rate, price of oil or price of diesel, and related independent variables in the regression. These results can assist in policy formulation and management, strategic marketing, product development, and tourism planning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".