Understanding the natural rate of interest for a small open economy
Bibliographic record
Abstract
In this paper, we develop a structural model to estimate the current level of the natural rate for a small open economy, featuring a rich set of shocks to provide economic intuition for its underlying drivers. The model follows the New Keynesian tradition with several frictions and is able to draw implications for a monetary policy stance. In contrast to other DSGE models in the literature, this framework includes two main blocks-one related to the foreign sector and one associated with the local economy, linked by the uncovered interest rate parity condition. With this structure, the natural rate is affected by local and external factors, disaggregated in permanent and transitory shocks. Using Bayesian techniques, the model estimates the natural interest rate for two example cases, Mexico and Canada, considering data from these economies and the United States. Results show that the US economy is relevant to explaining natural rates in both countries. For the Mexican case, the drivers are shocks to the US risk premium and the marginal efficiency of investment, as well as country risk premium variations. For Canada, shocks to the households' discount factor play an important role.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".