Why U.S. Money does not Cause U.S. Output, but does Cause Hong Kong Output
Bibliographic record
Abstract
Standard econometric tests for whether money causes output will be meaningless if monetary policy is chosen optimally to smooth fluctuations in output. If U.S. monetary policy were chosen to smooth U.S. output, we show that U.S. money will not Granger cause U.S. output. Indeed, as shown by Rowe and Yetman (2000), if there is a (say) 6 quarter lag in the effect of money on output, then U.S. output will be unforecastable from any information set available to the Fed lagged 6 quarters. But if other countries, for example Hong Kong, have currencies that are fixed to the U.S. dollar, Hong Kong monetary policy will then be chosen in Washington D.C., with no concern for smoothing Hong Kong output. Econometric causality tests of U.S. money on Hong Kong output will then show evidence of causality. We test this empirically. Our empirical analysis also provides a measure of the degree to which macroeconomic stabilisation is sacrificed by adopting a fixed exchange rate rather than an independent monetary policy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".