Bibliographic record
Abstract
One of the major sectors that have been experiencing rapidly increasing economic growth in Mexico is the Tourism sector. This research study aims to inquire into the contribution of tourism development on economic growth in Mexico, where number of tourist arrivals is dependent on exchange rate and GDP per capita. To make this research study more precise, we use the GDP per capita of Brazil, Canada, Colombia and United States of America separately which are among the top 10 tourist countries who visits Mexico for tourism (WTO, 2014). After running the stationarity test, we ran the Johansen cointegration test to know if there is a long run relationship among the three variables. We found out that all the results for the four countries indicate two cointegration vectors using the trace test. After knowing the cointegration of the vectors, we ran the VECM to investigate the long run causality of the series. The Error Correction Term shows that there is a long run causality running from exchange rate and GDP per capita of USA to the number of tourist arrivals in Mexico while the Error Correction Term shows that there is no long run causality running from exchange rate and the GDP per capita of Brazil, Canada and Colombia to the number of tourist arrivals in Mexico. After knowing the causality of the variables, we ran the residual diagnostic test of autocorrelation, heteroscedasticity and histogram and normality where we found out the absence of autocorrelation, heteroscedasticity and residuals were normal distributed for all the countries and variables.\nKeywords: Tourism, Economic growth, Johansen Cointegration, VECM, Residual diagnostics test.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".