Bibliographic record
Abstract
We evaluate the empirical evidence for costs that penalize changes in investment using U.S. industry data. In aggregate models, such investment adjustment costs have been introduced to help account for a variety of business cycle and asset market phenomena. So far no attempt has been made to estimate these costs directly at a disaggregated level. We consider an industry model with investment adjustment costs and estimate its parameters using generalized methods of moments. The findings indicate small costs associated with changing the flow of investment at the industry level. The weighted average of the industry elasticities with respect to the shadow price of capital, which depends inversely on the adjustment cost parameter, is eight times larger than the largest estimate reported in Levin et al. (2006) . We examine a variety of factors that may account for this discrepancy, but a substantial part of it remains unexplained. Our results therefore suggest that more caution is needed when giving policy advice that hinges on a structural interpretation of large investment adjustment costs.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".