Kvalita inflačních prognóz České národní banky
Bibliographic record
Abstract
The aim of this thesis is evaluate quality of inflation forecasts from Czech national bank (CNB). Czech national bank started to create those forecasts after changing to new model of aiming inflation in 1998. The forecasts are issued by CNB quarterly for following 7 quarter years. There will be description of terms that are connected with and that are influencing creating forecasts of CNB, in the theoretical part. Attention will be given to inflation, Czech national bank, its forecasting system and prediction model need there. The main part of work will be dedicated to inflation forecast quality, that are issued be CNB. Quality of up to date forecasts will be evaluated according to various criteria. Among the criteria there are coefficient of determination, mean error, mean sqauarred error, Theil's Inequality coefficient and confidence interval. The results show that the forecasts haven not good describing ability in the long time horizon. It is rather difficult for CNB to estimate inflation tendency correctly in such a long time, because it cannot influence unexpected changes of exogen factors. Forecasts into half year should be regarded as of good quality.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".