Seasonal Quasi-Vector Autoregressive Models with an Application to Crude Oil Production and Economic Activity in the United States and Canada
Bibliographic record
Abstract
We introduce the Seasonal-QVAR (quasi-vector autoregressive) model that we apply to study the relationship between oil production and economic activity. Seasonal-QVAR is a score-driven nonlinear model for the multivariate t distribution. It is an alternative to the basic structural model that disentangles local level and stochastic seasonality. Seasonal-QVAR is robust to extreme observations and it is an extension of Seasonal-VARMA (VAR moving average). We use monthly data from world crude oil production growth, global real economic activity growth and the industrial production growths of the United States and Canada. We address an important economic question about the influence of world crude oil production on the industrial productions of the United States and Canada. We find that the effects of industrial production growth of the United States on world crude oil production growth are about six times higher for the basic structural model and Seasonal-VARMA than for Seasonal-QVAR. We also find that the effects of world crude oil production growth on the industrial production growth of Canada are positive for Seasonal-QVAR, but those effects are negative for Seasonal-VARMA. Likelihood-based performance metrics and transitivity arguments support the estimates of Seasonal- QVAR, as opposed to the basic structural model and Seasonal-VARMA.
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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 | 0.000 |
| 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 | 0.000 |
| 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".