The Business Cycle, Inflation, and Unemployment Rate Nexus: An Empirical Approach
Bibliographic record
Abstract
This paper revisits the main assumption regarding the original Phillips curve regarding the American economy, in which one assumes that the unemployment rate causes an inflation rate. In this context, this paper aims to evaluate if the variance of the inflation rate affects the unemployment rate and, besides, if there is a one-way causality from the variance of the inflation rate to the unemployment rate. Based on quarterly time series from 1959:04 to 2019:04 the empirical results show, via OLS and GMM methods, that the monetary policy affects the business cycle, and, in turn, the business cycle impacts the unemployment rate. Hence, the monetary policy affects indirectly the unemployment rate via the business cycle. On the other hand, the variance of the inflation rate contributes to an increase in the unemployment rate, consequently, there isn’t a trade-off between the unemployment rate and the variance of the inflation rate. Moreover, there is a one-way causality from the variance of the inflation rate to the unemployment rate. This is the contribution of this paper. At last, based on the Phillips curve, one expects that the unemployment rate causes the inflation rate. However, the Granger causality tests display a two-way causality relation between both variables.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".