A Discussion of the Reliability of Results Obtained with Long-Run Identifying Restrictions
Bibliographic record
Abstract
In a recent article, Faust and Leeper (1997) discuss reasons why inference from structural VARs identified with long-run restrictions may not be reliable. In this paper, the authors argue that there are reasons to believe that Faust and Leeper's arguments are not devastating in practice. First, simulation exercises suggest that this approach does well when used with data generated with standard macroeconomic models. Second, empirical applications suggest that it gives results that are much more robust than would be implied by Faust and Leeper's main proposition. A reasonable approach would appear to be, therefore, to follow Sims' (1971; 1972) and Dufour's (1997) recommendation and to present robustness checks, allowing readers to judge for themselves what the effects of possible approximation errors might be.
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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 | 0.108 | 0.485 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".