Bibliographic record
Abstract
What are the effects of financial market imperfections on the fluctuations in unemployment and vacancies in the labor market? Standard DSGE models are silent about this since they have not modeled unemployment directly. In this paper I augment a standard monetary DSGE model with explicit financial and labor market frictions. The financial frictions are modeled as Bernanke, Gertler and Gilchrist (1999). The labor market frictions are modeled as by a search framework with staggered contracting in nominal wages (Gertler and Trigari (2006)). Assuming the economy is subject to various shocks, including a shock that originates in the financial sector, I estimate the model on the U.S. data. The preliminary results show that the model accounts well for the cyclical behavior of real wage, unemployment and vacancies observed in the U.S. data. The model also accounts well for the cyclical behavior of external finance premium. The simulation results suggest that financial accelerator play an important role in propagating the financial shock. A negative financial wealth shock leads to a rise in unemployment and a decline in vacancy posting. With the financial accelerator mechanism, the financial shock contributes more than 30 percent of the fluctuations in unemployment and vacancies. JEL: classification: E31;E32; E52
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.002 |
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".