Another look at the linear <i>q</i> model: an empirical analysis of aggregate business capital spending with maintenance expenditures
Bibliographic record
Abstract
Abstract The paper revisits the empirical investment literature, which has established that aggregate business fixed investment is not found to be related linearly to marginal or average Tobin's q . The theoretical background is extended here by developing a supply‐side model where the depreciation rate of private capital is determined endogenously. The firm can either invest in ‘new’ capital, which adds directly to the existing capital stock at the presence of convex adjustment costs, or extend the durability of installed capital through maintenance expenditure, which affects its depreciation rate. The model shows that Tobin's q is then a positively related sufficient statistic for both components of aggregate capital expenditures. This central implication is tested empirically using aggregate time‐series survey data from Canada on ‘new’ investment and maintenance expenditures covering the period 1956–93. The estimated relationships produce significant and plausible parameter estimates for the structural parameters of the q model.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".