Structural and forecasting softwood lumber models with a time series approach
Bibliographic record
Abstract
The development of cointegration theories and the presence of nonstationarity in time series raised serious concerns about possible spurious estimations in forest products models. Based on the results of Hsiao (1997a, 1997b), all the virtues of two-stage least square (2SLS) hold if there are sufficient cointegration relations. Stationary null and nonstationary null unit root tests and monthly seasonal unit root tests were applied to the time series used in this dissertation. Cointegration tests with exogenous variables were performed to justify the 2SLS. A regional error correction model (ECM) with four regional lumber supply and demand equations and a U.S.-Canada supply and demand ECM were estimated. CUSUM tests did not find any structural changes. Both estimated models showed that the imported Canadian lumber and the U.S. lumber are substitutes. The estimated long-run and short-run own-price elasticities for demand and supply are inelastic for all the equations but the short-run supply equation for the West Coast. The long-run lumber supply equations have significant trends: annually -3% for the Inland West and 2% for the other regions. The popular maximum likelihood estimation for the restricted ECM cannot pass the test for the restrictions and is, therefore, not used for the regional structural lumber model. A series of univariate and multi-equation models were used as forecasting models. A combination of univariate model were shown to be the best forecasting models for lumber prices, and a combination of univariate and multi-equation models were shown to be the best forecasting models for lumber quantities. The selected combinations of models were shown to be the best with additional observations. It was also shown that lumber quantities could be forecasted better than lumber prices.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".