Investment-Specific News Shocks and U.S. Business Cycles
Bibliographic record
Abstract
This paper provides robust evidence that news shocks about future investment-specific technology (IST) constitute a signicant force behind U.S. business cycles. Extending a recent empirical approach to identifying news shocks, we find that positive IST news shocks induce comovement, i.e., raise output, consumption, investment, and hours. These shocks account for 70% of the business cycle variation in output, hours, and consumption, and 60% of the variation in investment, and have played an important role in 9 of the last 10 U.S recessions. IST news shocks also dominate unanticipated IST shocks in accounting for the forecast variance of aggregate variables. The findings have two important implications for research on news driven business cycles. First, they provide strong support for shifting focus to IST news shocks when investigating the role of news (or foresight) in driving business cycles. Second, an important avenue for further research is to consider structural mechanisms that can enhance the role of IST news shocks in estimated dynamic general equilibrium models.
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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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".