Duration Dependence in US Expansions: A re-examination of the evidence
Bibliographic record
Abstract
It is commonly accepted that economic expansions do not exhibit duration dependence, that is, the probability of an expansion terminating in the near future is thought to be independent of the length of the expansion. Our main focus is on determining the probability of the US economy entering a recession in the following year (or following two years) conditional on the expansion having lasted q quarters. When looking at the probability of entering a recession within a year (or 2 years), we find considerable evidence of economically significant duration dependence, especially when adopting a non-parametric approach. For example, for an expansion that has lasted only 5 quarters, the probability of entering a recession in the next year is around 10%, while this increases to 30-40% if the expansion has lasted over 35 quarters. Similarly, if looking at a two years window, we find the probability of entering a recession in the next two years raises from 25-30% to around 50-80% as the expansion extends from 5 quarters to 32 quarters. This pattern suggests that certain types of macroeconomic vulnerabilities may be accumulating as the expansion ages causing the arrival of a recession to become more likely. Our non-parametric estimates suggest that this later pattern is especially important once a recession has lasted more than 6 years.
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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 | 0.011 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".