Forecasting Inbound Canadian Tourism: An Evaluation of Error Corrections Model Forecasts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper computes and evaluates a variety of quantitative forecasts for inbound Canadian tourists, including the Error Corrections Model (ECM) and the traditional regression model forecasts. A number of forecasting methods are employed: naive to sophisticated, univariate to multivariate, time series and econometric. Forecasts for the number of inbound Canadian tourists are derived using data from four major markets: the USA, the UK, Germany and Japan. The evaluation of the forecasts is based on the Generalized Forecast Error Second Moment (GFESM) criterion developed by Clements and Hendry (1993) and the Adjusted Mean Absolute Percentage Error (AMAPE) criterion. The ECM forecasts performed best, while the traditional regression model forecasts performed poorly. In this study, using Canadian data, the development of an ECM (which entails careful analysis of the integration and co-integration properties of the variables) provides an improvement in forecast accuracy. Previous tourism studies have found less promising results concerning the performance of the ECM forecasts.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 it