Yield Curve and Monetary Policy Expectations in Small Open
Bibliographic record
Abstract
This paper estimates a New Keynesian dynamic stochastic general equilibrium (DSGE) model in small open economies using the yield curve data as well as standard macro data. The DSGE model is estimated on the data of three inflation-targeting small open economies (Australia, Canada, and New Zealand) using Bayesian methods. We find that the long-end of the yield curve is highly correlated with the current and future short-term interest rates determined by domestic central banks. Yield curve data are particularly informative about the future stance of monetary policy in Australia and Canada in that the correlation between the model-implied monetary policy expectations and the ex-post realized policy interest rates increases when the yield curve data are used in estimation. Unlike the estimation results solely based on the macro data that imply the cental bank’s relatively strong focus on inflation stabilization, our results using yield curve information suggest that even inflation-targeting central banks have a significant concern for output stabilization. We also document that persistent domestic shocks, not foreign disturbances, drive the average level of the yield curve in these three countries.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".