DETERMINANTS OF U.S. OUTBOUND TOURISM TO CANADA, MEXICO AND WESTERN EUROPE: EMPIRICAL EVIDENCE FROM AN AUTOREGRESSIVE DISTRIBUTED LAG MODEL
Bibliographic record
Abstract
This article investigates U.S. outbound tourism demand for selected major foreign destinations, namely Mexico, Canada and Western Europe, using the Autoregressive Distributed Lag model (ARDL) and quarterly data during the period 1995Q4 -2016Q4. No study in the tourism literature conducted an empirical analysis on U.S. outbound tourism demand, the largest source of tourist arrivals in foreign destinations in the world, using ARDL modelling. Results from the Augmented Dickey-Fuller unit root testing methods ruled out that none of the variables under consideration, in this study, is in the order of I(2): both indicated that all the time series are integrated in the order of, either I(0), or I(1). The ARDL bounds tests reveal the existence of a long-run equilibrium relationship between the number of U.S. tourists' arrivals, relative prices of tourism in these selected foreign destinations, transportation costs, real U.S. personal disposable income, and real U.S. median home price as proxy for wealth. The estimated coefficients of the short-run dynamic ARDL models are negative as expected, and significant; thus, indicating that short-term deviations, due to shocks, are restored back into equilibrium from one period to the next at the rate of 46%, 87% and 91% for Mexico, Western Europe and Canada, respectively. Results of the CUSUM and CUSUMSSQ stability tests show that the models have remained relatively stable over the course of the study period. Overall, the findings provide useful insights for lawmakers and tourism management practitioners in those countries on how to develop policies aimed at promoting their tourism industries in order to achieve desired national goals.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".