Does the Length of the Period Matter for the Identification and the Modelling of Monetary Policy Shocks
Bibliographic record
Abstract
In this Paper, we ask whether our empirical and theoretical knowledge about the effect of monetary policy shocks is robust to the choice of the period length. We think that such a question is particularly relevant in the monetary literature, as frictions are often introduced under the form of a one-period lag in agents' reaction. We first show that it is possible to use more efficiently the available information when identifying monetary policy shocks. Using together quarterly series for GDP and monthly series for monetary aggregates and interest rates, it is possible to identify monetary shocks with the assumption that they do not have any impact on GDP within a month, by restricting ourselves to the identification of third-month-of-a-quarter shocks. With this new method, we obtain very similar estimated IRFs, as compared with the results obtained with quarterly data, although the price puzzle appears to be more pronounced in our estimates. Such a similarity is a new fact that quantitative models need to match. In the second part of the Paper, we propose a model-based explanation for this result, by computing a limited participation model predictions, when the time period is reduced from one quarter to one month, and when the model predictions are time-aggregated at the quarterly frequency. We show that the introduction of adjustment costs to portfolio reallocation into the model is not only improving its fit, but is necessary for obtaining qualitatively realistic predictions, when the length of the period is thought to be the month and not the quarter.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".