Technology Shock and the Business Cycle in the G7 Countries: A Structural Vector Error Correction Model
Bibliographic record
Abstract
This paper investigates the importance of technology shock in explaining fluctuations over business cycles and its contractionary effects. Applying the SVEC model on quarterly data of G7 countries and accounting for long cycles in hours worked, there is evidence of a decline in employment as measured by hours worked and investment following a positive technology shock. Hours worked show a persistent decline in France and UK, and this lasts for seven years in Italy, three years in Japan, two years in the USA and Canada; and one year in Germany. However, our findings suggest that technology shocks may play only a limited role in deriving the business cycles in the G7 countries; for they only account for under 30 percent of the business cycle variation in hours and investment, under 35 percent of the business cycle variation in consumption, and under 50 percent of the business cycle variation in output of most of the G7 countries. Our findings do not support the conventional real business cycle interpretation; instead, they are consistent with the predictions of the sticky-price model.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".