Overnight Rate Innovations as a Measure of Monetary Policy Shocks in Vector Autoregressions
Bibliographic record
Abstract
The authors examine the Bank of Canada's overnight rate as a measure of monetary policy in vector autoregression (VAR) models. Since the time series of the Bank's current measure of the overnight rate begins only in 1971, the authors splice it to day loan rate observations to obtain a sufficiently long period of data. The resulting series, called Ron, extends back to the 1950s. The authors' analysis yields four findings of interest: First, Ron innovations and innovations of the Bank's current overnight rate measure appear to incorporate virtually identical information about monetary policy shocks. Second, the path of Ron innovations provides a reasonable account of the evolution of monetary policy actions over the past 35 years. Third, shocking Ron in VAR systems has consequences for output, prices and the exchange rate that might be expected from a monetary policy shock. Finally, as a monetary policy variable in these VAR systems, Ron performs at least as well as either the 90-day paper rate or the term spread. The main conclusions are that Ron, the overnight rate variable developed by the authors, provides a good basis for measuring monetary policy actions in VAR-based analysis and that Ron innovations can provide a good measure of monetary policy shocks.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".