Reviving the Limit Cycle View of Macroeconomic Fluctuations
Bibliographic record
Abstract
There is a long tradition in macroeconomics suggesting that market imperfections may explain why economies repeatedly go through periods of booms and busts, with booms sowing the seeds of the subsequent busts.This idea can be captured mathematically as a limit cycle.For several reasons, limit cycles play almost no role in current mainstream business cycle theory.In this paper we present both a general structure and a particular model with the aim of giving new life to this mostly dismissed view of fluctuations.We begin by showing why and when models with strategic complementarities-which are quite common in macroeconomics-give rise to unique equilibrium dynamics characterized by a limit cycle.We then develop and estimate a fully-specified dynamic general equilibrium model that embeds a demand complementarity to see whether the data favors a configuration supportive of a limit cycle.Booms and busts arise endogenously in our setting because agents want to concentrate their purchases of goods at times when purchases by others are high, since in such situations unemployment is low and therefore taking on debt is perceived as being less risky.A key feature of our approach is that we allow limit-cycle forces to compete with exogenous disturbances in explaining the data.Our estimation results indicate that US business cycle fluctuations in employment and output can be well explained by endogenous demand-driven cycles buffeted by technological disturbances that render those fluctuations irregular.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".