DRAFT, PRELIMINARY, COMMENTS ARE APPRECIATED. Simple Rules in the M1-VECM *
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper analyses various simple interest rate rules using a vector error correction forecasting model of the Canadian economy that is anchored by long-run equilibrium relationships suggested by economic theory. Dynamic and stochastic simulations are performed using several interest rate rules, including money based rules and their properties are analysed. Among the class of rules we consider in this model, we find that a simple rule with interest rate smoothing minimizes the volatility of output, inflation and interest rate. This rule dominates Taylor-type, Ball and other simple rules. * FR-01-002.We would like to thank Scott Hendry, Dinah Maclean, Pierre St-Amant for helpful suggestions and discussions. Thank you also to Sharon Kozicki our discussant at the CEA 2001 meetings in Montreal, Chris Graham for providing technical help, Jim Day for providing help with the graphs and participants at the brown bag meeting. The views in this paper are those of the authors and should not be attributed to the Research on monetary policy rules has exploded in the last few years. Much of this research has focused on finding a simple benchmark rule that the central bank can use in its decision
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it