Expectations' Dispersion & Convergence towards Central Banks' IR forecasts: Chile, Colombia, Mexico, Peru & United Kingdom, 2004-2014
Bibliographic record
Abstract
The study evaluates the effect of both the publication of Inflation Report (IR)’s forecasts and the subsequent media diffusion efforts (made by 5 central banks) on (i) the dispersion of ‘fixed-event’ forecasts for inflation and real growth produced by the macroeconomic insiders of a country (and gathered by Consensus Economics, Inc.), as well as (ii) the distance between their median and the aforementioned official forecasts. The 5 central banks correspond to the monetary authorities in Chile, Colombia, Mexico, Peru and United Kingdom. Statistically testing the effects on the dispersion and distance uses a common sample of monthly forecasts from 2004 to 2014 and reach high specificity by using separate samples according to the forecasting horizon (short and medium ‘term’) and the macroeconomic uncertainty level (IR publication months are classified as either high- or low-uncertainty months). With a significance level of 10 per cent, the general results are that (a) increases and decreases in the dispersion can be attributed to either IR forecast publication or media diffusion; and (b) increases and decreases in the distance can be attributed to either IR forecast publication or media diffusion, although the number of increases in the distance is low relative to (a). \nComment from the author: It would be interesting to add results for more countries. Specifically, I was planning to add Canada and New Zealand. However, in the case of New Zealand, the corresponding series from Consensus Economics, Inc. is actually not available near Peru for the whole sample (the nearest one is actually located at the British Library!). There exists a critique addressing the econometric approach: it is related to the idea of causality and the need to use the difference-in-difference approach (this implies the need to include data from non-inflation-targeting countries). I am totally satisfied with the paper, though. In a nutshell, I consider more important to address the issue as if I were a medicine doctor wondering about whether the temperature is normal, high or low for the specific cases of 5 individuals instead of digressing about what is "normal temperature" for (say) 40 individuals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.046 | 0.002 |
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; both teacher heads agree on what is shown here.
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".