Determinants of demand for wood products in the US construction sector: an econometric analysis of a system of demand equations
Bibliographic record
Abstract
A system of conditional demand equations for wood products in the US construction sector is derived from the relevant end-use cost functions by taking advantage of the duality between the production function and the cost functions. Seven different inputs, including five wood products, labor, and capital, and three different end uses (new housing construction, repair and remodeling, and nonresidential construction) are analyzed. A seemingly unrelated regression estimation procedure is employed using a detailed annual time series database that spans the period from 1950 to 2009. Results suggest large variability in own-price long-term demand elasticity among wood inputs and across end uses. Softwood lumber, for example, is highly inelastic for new housing construction, inelastic for repair and remodeling, and nearly unitary elastic for nonresidential construction. Softwood plywood, on the other hand, is elastic for new housing construction and highly inelastic for nonresidential construction. In addition, we find that dynamic adjustments to long-term conditional intensity factor demand are prevalent across all inputs and among all three end-use categories.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".