Boom–Busts in Asset Prices, Economic Instability, and Monetary Policy
Bibliographic record
Abstract
INTRODUCTION The link between monetary policy and asset price movements has been of perennial interest to policy makers. The 1920s stock market boom and 1929 crash and the 1980s Japanese asset bubble are two salient examples in which asset price reversals were followed by protracted recessions and deflation. The key questions that arise from these episodes is whether the monetary authorities could have been more successful in preventing the consequences of an asset market bust or whether it was appropriate for the authorities only to react to these events ex post. In this chapter we consider the potential cases for proactive versus reactive monetary policy based on the situation in which asset price reversals can have serious effects on real output. Our analysis is based on a stylized model of the dilemma with which the monetary authorities are faced in asset price booms. On the one hand, letting the boom go unchecked entails the risk that it will be followed by a bust accompanied by a collateral-induced credit crunch. Restricting monetary policy can be thought of as insurance against the risk of a credit crunch. On the other hand, this insurance does not come free: Restricting monetary policy implies immediate costs in terms of lower output and inflation. The optimal monetary policy depends on the relative cost and benefits of the insurance. Although the model is quite stylized, we find that the optimal monetary policy depends on the economic conditions – including the private sector's beliefs – in a rather complex way.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".