Estimating Regional Softwood Lumber Supply in the United States Using Seemingly Unrelated Regression
Bibliographic record
Abstract
In this article, we present estimates of regional softwood lumber supply functions in the United States using annual time series data for 1959 to 2009. Seemingly unrelated regression is used in a profit maximization framework to model softwood lumber supply as a function of lumber and stumpage prices, lagged supply, wage rate, and interest rate for the eastern and western United States. The effects of listing the northern spotted owl (Strix occidentalis caurina) as a threatened species and the US–Canada softwood lumber trade dispute are controlled for in empirical estimation. Results show that regional lumber supply is quite inelastic to lumber price and that stumpage price and bank prime rate negatively influence regional lumber supply. Results also suggest that present market supplies of softwood lumber have potential expansionary influence on future supplies, that listing of the northern spotted owl in 1990 reduced the lumber supply in the western region during subsequent years, that the US–Canada softwood lumber trade dispute/agreements favored regional lumber production in the United States during the period from 1996 to 2005, and that supply has declined during the recent period of economic recession.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".