A Horse Race of Monetary Policy Regimes: An Experimental Investigation
Bibliographic record
Abstract
Monetary policy has evolved significantly in the aftermath of the global financial crisis and the COVID-19 pandemic. Alternative approaches such as average inflation targeting (AIT), price-level targeting (PLT) and nominal GDP-level targeting (NGDP) have gained attention in academic and policy discussions. These history-dependent regimes can be powerful and effective in stabilizing an economy because the central bank has to make up for past misses. However, their performance depends on forward-looking expectations and people’s understanding of how these regimes work. Despite the recent attention these alternative approaches to monetary policy have received, evidence of the public’s understanding remains limited. Using a unified experimental framework, we provide a comprehensive assessment of five monetary policy regimes: inflation targeting (IT), dual mandate (DM), AIT, PLT and NGDP. We study how effectively people can understand and form macroeconomic expectations under the different policy frameworks, both throughout periods of economic stability away from the effective lower bound (ELB) and during demand-driven recessions at the ELB. We take the simple New Keynesian model used in the Bank of Canada's own theoretical analysis to the laboratory and run an experimental horse race of the five alternatives. Our results suggest a distinct ranking of the monetary policy regimes in terms of their ability to achieve macroeconomic stability. Rate-targeting approaches such as IT, DM and AIT significantly outperform level-targeting regimes such as PLT and NGDP in minimizing deviations of inflation, the output gap and nominal interest rates from target. IT, DM, and AIT with a short four-quarter horizon deliver similar performances. AIT with a shorter horizon (4 quarters) performs better than with a longer horizon (10 quarters). Monetary policy regimes that are framed around the inflation rate (e.g., AIT with a 10-quarter horizon) are found to deliver significantly more stable economic outcomes than those that target price levels (PLT).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".