Bibliographic record
Abstract
Equity price is cyclical and often leads the business cycle by one or two quarters. These observations lead to the hypothesis that shocks to equity market liquidity are an independent source of the business cycle. In this paper I construct a model to evaluate this hypothesis. The model is easy for aggregation and for the construction of the recursive competitive equilibrium. After calibrating the model to the US data, I find that a negative liquidity shock in the equity market can generate large drops in investment and output but, contrary to what one may conjecture, the shock generates an equity price boom. This response of equity price occurs as long as a negative liquidity shock tightens firms' financing constraints on investment. Thus, liquidity shocks to the equity market cannot be the primary driving force of the business cycle. For equity price to fall as it typically does in a recession, a negative liquidity shock must be accompanied or caused by other shocks that reduce the need for investment sufficiently and relax firms' financing constraints on investment. I illustrate that a strong negative productivity shock is a good candidate of such concurrent shocks.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".