A Model of Canadian Oil and Gas Price Fluctuations - 2012 Update
Bibliographic record
Abstract
Abstract In 2005 a series of statistical calculations were presented for Canadian hydrocarbon prices [1]. There were two main conclusions: long-term historical data indicates that hydrocarbon prices tend to revert back to historical averages, short-term price fluctuations are unpredictable. It is more clear than ever that short-term prices are unpredictable, but this paper will attempt to demonstrate once more that mean reversion should be included in any long-term model. This paper demonstrates that any discussion of oil and gas prices in Canada must consider inflation. Several different means of adjusting for inflation are presented but all show that Canadian hydrocarbon prices are strongly variable, but mean reverting. This paper also argues that, while convenient, discussing the price of a commodity in terms of only one currency ignores changes in the relative value between currencies and basis differentials. These factors can have significant economic impact. This paper updates the previous price fluctuation model for prices up to the end of 2012. As before, the model incorporates a random walk with mean reversion that was developed and tuned to fit Canadian hydrocarbon prices. Starting with the current spot price, the model will generate a random but equiprobable prediction of future prices. The model can be used as input into a Monte-Carlo simulation. Alternately, the model can be run multiple times in order to generate "high", "low", and "expected" price predictions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".