Inventories and the Business Cycle: An Equilibrium Analysis of (S,s) Policies
Bibliographic record
Abstract
We develop an equilibrium business cycle model where nonconvex delivery costs lead producers of final goods to follow generalized (S,s) inventory policies with respect to intermediate goods.When calibrated to match the average inventory-to-sales ratio in postwar U.S. data, our model reproduces two-thirds of the cyclical variability of inventory investment.Moreover, inventory accumulation is strongly procyclical, and production is more volatile than sales, as in the data.The comovement between inventory investment and final sales is often interpreted as evidence that inventories amplify aggregate fluctuations.Our model contradicts this view.Despite the positive correlation between sales and inventory investment, we find that inventory accumulation has minimal consequence for the cyclical variability of GDP.In equilibrium, procyclical inventory investment diverts resources from the production of final goods; thus, it dampens cyclical changes in final sales, leaving GDP volatility essentially unaltered.Moreover, although business cycles arise solely from shocks to productivity and markets are perfectly competitive in our model, it nonetheless yields a countercyclical inventory-to-sales ratio.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".