Is the Time Ripe for Price-Level Path Stability?
Bibliographic record
Abstract
In this chapter we provide a critical and selective survey of arguments relevant for the assessment of the case for price-level path stability (PLPS). Using a standard, hybrid new Keynesian model, we argue that price-level stability provides a natural framework for monetary policy under commitment. There are two main arguments in favor of a PLPS regime. First, it helps overall macroeconomic stability by making expectations operate like automatic stabilizers. Second, under a PLPS regime, changes in the price level operate like an intertemporal adjustment mechanism, reducing the magnitude of required changes in nominal interest rates. Such a property is particularly relevant as a means to alleviate the importance of the zero bound on nominal interest rates. We also review and discuss the arguments against PLPS. Finally, we also demonstrate, using the Smets and Wouters (2003) model that includes a wide variety of frictions and is estimated for the euro area, that the price level is stationary under optimal policy under commitment for a particular loss function. Specifically, the results obtained when the quasi-difference of inflation is used in the loss function, as in the hybrid new Keynesian model. Overall, the arguments in favor of or against PLPS depend upon the degree of dependence of private-sector expectations on the characteristics of the monetary policy regime. Introduction According to the conventional wisdom in central banking circles, PLPS is not an appropriate goal to delegate to an independent central bank. There is strong intuition behind this claim.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".