Neutral Real Interest Rate, Monetary Policy and Business Cycle: Using the Kalman Filter and a Counterfactual Structural Approach for a Large Emerging Market
Bibliographic record
Abstract
Central banks monitor the evolution of economic aggregates in order to enhance the effectiveness of monetary policy, within the context of inflation targeting regimes. This paper assesses whether controlling or not the path of the neutral real interest rate alters business cycle responses to structural shocks in monetary policy. For this purpose, the study uses the Kalman filter to extract the latent variable of neutral interest rates, as well as a counterfactual exercise based on parsimonious SVAR models, both applied to Brazil. The evidence suggests that omitting the trajectory of the neutral real rate introduces biases in the estimated stochastic responses of economic activity to unanticipated changes in monetary policy. Quantitatively, this omission results in a loss of approximately 5% of the estimated marginal effects in the first 12 months, in addition to bringing forward the initial impacts by 6 months, and a loss of up to 70% in the estimated effects over a 24-month horizon. Such results were robust for the inclusion of the US long term real interest rate and the country risk perception.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".