Bibliographic record
Abstract
Recent work based on sticky price-wage estimated dynamic stochastic general equilibrium (DSGE) models suggests investment shocks are the most important drivers of post-World War II US business cycles. Consumption, however, typically falls after an investment shock. This finding sits oddly with the observed business cycle comovement where consumption, along with hours-worked and investment, moves with economic activity. We show that this comovement problem is resolved in an estimated DSGE model when the cost of capital utilization is specified in terms of increased depreciation of capital, as originally proposed by Greenwood et al. (1988) in a neoclassical setting. Traditionally, the cost of utilization is specified in terms of forgone consumption following Christiano et al. (2005), who studied the effects of monetary policy shocks. The alternative specification we consider has two additional implications relative to the traditional one: (i) it has a substantially better fit with the data and (ii) the contribution of investment shocks to the variance of consumption is over three times larger. The contributions to output, investment, and hours, are also relatively higher, suggesting that these shocks may be quantitatively even more important than previous estimates based on the traditional specification.
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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 | 0.006 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".