Putting the Cycle Back into Business Cycle Analysis
Bibliographic record
Abstract
This paper begins by re-examining the spectral properties of several cyclically sensitive variables such as hours worked, unemployment and capacity utilization.For each of these series, we document the presence of an important peak in the spectral density at a periodicity of approximately 36-40 quarters.We take this pattern as suggestive of intriguing but little-studied cyclical phenomena at the long end of the business cycle, and we ask how best to explain it.In particular, we explore whether such patterns may reflect slow-moving limit cycle forces, wherein booms sow the seeds of the subsequent busts.To this end, we present a general class of models, featuring local complementarities, that can give rise to unique-equilibrium behavior characterized by stochastic limit cycles.We then use the framework to extend a New Keynesian-type model in a manner aimed at capturing the notion of an accumulation-liquidation cycle.We estimate the model by indirect inference and find that the cyclical properties identified in the data can be well explained by stochastic limit cycles forces, where the exogenous disturbances to the system are very short lived.This contrasts with results from most other macroeconomic models, which typically require very persistent shocks in order to explain macroeconomic fluctuations.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".