IDENTIFYING THE ROLE OF RISK SHOCKS IN THE BUSINESS CYCLE USING STOCK PRICE DATA
Bibliographic record
Abstract
I analyze the sources of U.S. business cycle fluctuations in an estimated Dynamic Stochastic General Equilibrium model with a rich set of nominal and real rigidities and various exogenous disturbances. The model includes a shock to the expected risk‐premium, which introduces a time‐varying wedge between the policy rate set by the central bank and the cost‐of‐capital of firms. In the aggregate data, most U.S. corporations finance their investment using internal funds, and stock prices reveal the opportunity cost of this type of financing. I therefore use corporate market value and dividend data in the Bayesian estimation of the model to identify risk shocks. Variance decomposition exercises show that these shocks account for a substantial part of the variation in the stock market, as well as the variation in output and investment, especially at short forecast horizons. The variation of these variables at longer forecast horizons are mainly captured by shocks to investment‐specific technological change. Historical decomposition points to the important role played by risk shocks in the run up of stock prices and output in the late 90s, and in the reversal of these variables in the early 2000s and during the recent recession. (JEL E32, E44)
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".