Central bank goals, institutional change and monetary policy: evidence from the United States and the United Kingdom
Bibliographic record
Abstract
Introduction A considerable empirical literature has emerged on the estimation of policy reaction functions and the identification of the underlying preferences of monetary authorities (see Groeneveld, Koedijk and Kool, 1996; Muscatelli and Tirelli, 1996; Clarida and Gertler, 1997; Clarida, Galí and Gertner, 1998; Favero and Rovelli, 1999; and Muscatelli, Tirelli and Trecroci, 1999). Some of these contributions examine whether recent changes in institutional structure, such as the shift to inflation targeting, have had an impact on the conduct of monetary policy. The evidence is mixed. For instance, Muscatelli, Tirelli and Trecroci (1999) show that there is only slight evidence that the introduction of inflation targeting affected forward-looking policy reaction functions in the United Kingdom, New Zealand, Sweden and Canada. In contrast they find some evidence of policy instability in Japan and the United States in the 1980s and 1990s, even in the absence of institutional change. Of course one would also expect significant shifts in monetary policy that bring about a reduction in inflation expectations to affect the transmission mechanism of monetary policy. The standard New Keynesian model of aggregate demand and supply, which has been used extensively for policy analysis (see Svensson, 1997; Rudebusch and Svensson, 1999; McCallum and Nelson, 1999a,b; and Rudebusch, 2000), suggests that forward-looking expectations are important on both the demand and the supply side.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".