What Affects the Predictions of Private Forecasters? The Role of Central Bank Forecasts
Bibliographic record
Abstract
This study analyses what affects the expectations of the private forecasters and, particularly, if they are influenced by the central bank's forecasts. The analysis uses data from the Economic Expectation Survey (EES), conducted by the Central Bank of Chile, and from the Monetary Policy Reports (IPoMs) covering the period 2001–2011. Short- and medium-term inflation expectations as well as short-term growth expectations are compared before and after the publication of a given issue of the IPoM, controlling for other factors, which may affect the expectations. These factors include Central Bank credibility, surprises in published data, and changes in the evaluation of the future interest rate, exchange rate and oil price. The results suggest that short-run inflation expectations (current year) of private forecasters are indeed influenced by the forecasts published by the central bank, mainly when these are lower and when they are published at the beginning of the year. They are also affected by surprises in published monthly inflation rates as well as by changes in the expectations for the exchange rate and monetary policy rate. The medium-term inflation expectations depend mainly on changes in short-run expectations, but oil price expectations and the future monetary policy rate also seem to matter. They are also influenced by central bank projections published in the last quarter of the year. The current year's GDP growth expectations in the EES are not affected by the central bank's forecasts as they are affected only by surprises in the monthly indicator of economic activity and the outlook for the monetary policy rate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".