Bibliographic record
Abstract
A central bank's main concern is the general direction of future inflation, and not transitory fluctuations of the inflation rate. As a result, this paper is concerned with forecasting a simple measure of the trend of inflation, the eight-quarter CPI-inflation rate. The primary objective is to improve the M1-based vector-error-correction model (VECM) developed by Hendry (1995), by imposing a set of equilibrium conditions to better anchor the long-run behaviour of interest rates, the exchange rate and the output gap in the model. These changes provide for greater confidence in the dynamic properties of the model, especially over a longer time horizon. This extended-VECM is shown to provide considerable leading information about inflation, forecasting the eight-quarter inflation rate with relatively small errors. The authors also stress that, to be most useful for monetary policy, inflation forecasts should explicitly indicate the range of uncertainty inherent in forecasting inflation with a long lead. For example, forecasts should explicitly consider confidence bands around forecasted outcomes, which is illustrated with the extended VECM developed in this paper. Finally, the paper emphasizes that monetary policy is probably best-served by an eclectic approach in which policy judgements are based on input from models that summarize different paradigms of the transmission mechanism, or that use different technical approaches.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".