Okun's Law Revisited：A Structural Change Approach
Bibliographic record
Abstract
The empirical validity of Okun's law has been well documented in the literature but the magnitude of the Okun's coefficient does not seem to reach a consensus. This study provides a modeling strategy to allow for the Okun's coefficients being different in distinct regimes using the multiple structural change model. The approach considered here has the advantages that it can directly determine the number of breaks and, in the same time, estimate the break dates along with the corresponding confidence intervals. Using data of U.S. and Canada, we have the following findings when four filtered methods are considered. First, overwhelming evidence in support of structural changes is found irrespective of countries considered and filtered approaches used. Second, the estimated break dates are nearly identical in most cases, implying that the results are robust to techniques used to extract the cyclical data. Third, the process of the unemployment rate exhibits positive persistence but no indication of nonstationarity is found. Fourth, the estimates of the contemporaneous Okun's coefficient show a substantial disparity in different regimes but the values are all negative in the right direction and mostly are statistically significant. Last, the long-run estimates of Okun's coefficient are always larger than (in absolute values) the short-run counterparts, suggesting that the (un) employment is more responsive to economic growth in the long run.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".