Comment on Harding and Pagan 'The econometric analysis of some constructed binary time series'
Bibliographic record
Abstract
This comment discusses Harding and Pagan's (2007) article that advocates modeling the NBER business cycle chronology as the outcome of the two-quarter rule. The comment shows that the two-quarter rule does not fare well as a description of the decision-making of the NBER with real-time data available at the time the NBER declared the turning points. In addition, it is not clear how generally one could posit tractable rules-such as the two-quarter rule-for other constructed binary time series, such as stock market booms and busts. As an alternative to modeling the NBER chronology per se, this comment suggests a modified Qual VAR that includes autoregressive dynamics in the latent business cycle index. Out-of-sample forecast results from this model look promising with real-time data without the econometric shortcomings highlighted in Harding and Pagan's critique of the literature to date.
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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 | 0.012 | 0.077 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.027 | 0.041 |
| Insufficient payload (model declined to judge) | 0.009 | 0.009 |
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".