The Reserve Bank of New Zealand’s Output Gap Measure in Real Time, 1997–2004
Bibliographic record
Abstract
This paper addresses the real-time versus ex-post properties of the output gap as quantified by the Reserve Bank of New Zealand’s “multivariate ” (MV) filter, starting with the second quarter of 1997, when the current procedure was implemented. Based on historical performance the MV-filtered output gap can produce reasonably reliable short term forecasts of nontradables CPI inflation. This is true for both ex-post and real-time MV-filtered output gap data. Nevertheless, historical output gap estimates do not converge to a final value. Apart from the end point problem with symmetrical filters and data revisions, modifications to the MV filter are a third source for subsequent output gap revisions. Moreover, for the period under consideration, the real-time MV-filtered output gap measure has not experienced less subsequent revisions than had the HP filter been used. Furthermore, based on the starting values from the MV filter, the Reserve Bank of New Zealand’s structural macro model has generated reasonably reliable forecasts of the output gap for only one quarter ahead. Two quarters ahead, the forecasts are at best marginally significant, and beyond that, they are uninformative. Finally, we can specify ex-post indicator based models of non-tradables inflation that have both longer leads and are more reliable than output gap based forecasts. Affiliation:
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".