The Role of Money in Two Alternative Models: When is the Friedman Rule Optimal, and Why?
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Bibliographic record
Abstract
In models of money with an infinitely lived representative agent (ILRA models), the optimal monetary policy is almost always the Friedman rule. In overlapping generations (OG) models, by contrast, the Friedman rule may not be optimal. In this paper, we use this difference in monetary policy prescriptions to help us identify and study the key difference between these two models as models of money. We study the welfare properties of monetary policy in a simple OG model under two very different money demand specifications and two alternative assumptions about the generational timing of taxes for money retirement. We conclude that the key difference between ILRA and OG monetary models is that in the latter, the standard method for constructing a monetary regime causes transactions involving money to become intergenerational transfers. Under alternative government fiscal/monetary regimes that offset these intergenerational transfers, the Friedman rule is always optimal.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 it