The International Propagation of News Shocks
Bibliographic record
Abstract
We address the question of business cycle co-movements within and between countries. We first show that for the U.S. and Canada as well as for Germany and Austria, a stock market innovation in the large country, that does not affect Total Factor Productivity in the short run, does indeed explain much of Total Factor Productivity changes in the long run. We therefore label such a shock a news about Total Factor Productivity of the large country. This shock is shown to act as a demand shock in the data, creating a boom in the large country as well as in the small one. Second, we propose a two-country-two-sector model that is shown to give realistic quantitative predictions. The model builds on the closed economy model of Beaudry and Portier [2004], in which there are limited possibilities to reallocate factors between investment and consumption good sectors. We also show that a canonical Real Business Cycle two-country model cannot account for those responses to technological news shocks we have identified in the data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".