Endogenous Monetary Policy and the Choice of Exchange Rate Regime
Bibliographic record
Abstract
Money has not played a great role in any of the chapters thus far. In Chapters 4 and 5 we linked nominal spending to the money supply via a cash-in-advance constraint, but the supply of money was given exogenously. In this chapter, at last, we give money an important role as a control variable that influences economic activity. We then use the model to address the issue of whether fixed exchange rates are desirable. For a survey of the literature on macroeconomic performance under alternative exchange rate regimes, see Alogoskoufis (1994). Canzoneri and Rogers (1990) discuss the costs and benefits of the European Union (EU) in particular. We briefly review the different ways of modeling supply and demand for money and the issues associated with exchange rate regimes in Section 9.1. In the remainder of the chapter, we formally analyze the issue of the desirability of fixed exchange rates in a model that contains money as well as a government and still has maximal resemblance to the models discussed in the preceding chapters. Section 9.2 describes the assumptions of this model, and the basic implications of the model are derived in Section 9.3. Section 9.4 examines the conditions under which the exchange rate will be constant over time, given optimal monetary policies. Money and Exchange Rate Regimes: A Review In this introductory section we first discuss alternative ways of modeling money demand.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".