Monetary Policy Evaluation in Real Time: Forward-Looking Taylor Rules Without Forward-Looking Data
Bibliographic record
Abstract
There is widespread agreement that monetary policy should be evaluated by using forward-looking Taylor rules estimated with real-time data. For the case of the U.S., this analysis can be performed using Greenbook data, but only through 2002. In countries outside the U.S., central banks do not regularly release their forecasts to the public. I propose a methodology for conducting monetary policy evaluation in real-time when forward-looking real-time data is unavailable. I then implement this methodology and estimate the resultant Taylor rules for the U.S., Canada, the U.K., and Germany. The methodology consists of calibrating models to closely replicate Greenbook forecasts, and then applying them to international real-time datasets. The results show that the U.S. output gap series is well described by quadratic detrending, while Greenbook inflation forecasts can be closely replicated using Bayesian model averaging over Autoregressive Distributed Lag models in inflation and the GDP growth rate. German and U.S. Taylor rules are characterized by inflation coefficients increasing with the forecast horizon and a positive output gap response. The U.K. and Canada interest rate reaction functions achieve maximum inflation response at middle-term horizons of about 1/2 year and the output gap coefficient enters the reaction functions insignificantly. Estimating the U.K. and Canadian Taylor rules as forward-looking is crucial, as backward-looking specifications produce nonsensical estimates. This is not the case for the U.S. and Germany.
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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 | 0.018 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".