Una Revisión de la Transmisión Monetaria y el Pass-Through en Chile
Bibliographic record
Abstract
First, this paper reviews the most important literature based on money VAR, which explains monetarytransmission mechanism and pass-through from depreciation to inflation in Chile and other countries. The focus is on (i) the length of the transmission lags from monetary policy to prices; (ii) the impact of monetary policy on output and real exchange rate; and (iii) the magnitude of pass-through coefficient. Second, we estimated four VAR models for the Chilean economy: three of them have short-run restrictions (SVAR) and the other one has long-run restrictions (VEC). Our estimations are different from other studies since we use an adjusted interest rate to consider the episodes of 1998, where the interbank interest rate was substantially higher than the monetary policy rate (MPR) announced by the Central Bank. This allows us to obtain a more precise identification of the monetary policy, thus the impact of this policy on prices and output was much lower than the effect found by other authors. Therefore, the monetary-transmission mechanism is characterized as follows; given a 100 basis points shock to interest rate during a quarter, inflation rate decreases by –0.2%, output by –0.4%, and there is a temporary appreciation of the real exchange rate by 0.4%. Additionally, monetary policy reaches its maximum effect between a year and a year and a half. On the other hand, pass-through coefficient turned out to be low and close to 18% after two years.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".