Bibliographic record
Abstract
This paper develops a macroeconomic model of the interaction between consumer debt and firm debt over the business cycle. I incorporate interest rate spreads generated by firm and household loan default risk into a real business cycle model. I estimate the model on US aggregate data. This allows me to analyse the quantitative importance of possible feedback effects between the debt levels of firms and households, and the relative contributions of financial and supply shocks to economic fluctuations. While firm level credit market frictions significantly amplify the response of investment to shocks, they do not amplify output responses. In general equilibrium, higher external financing spreads for households contribute to lower external financing spreads for firms, contrary to traditional Keynesian predictions. Furthermore, total factor productivity shocks remain an important source of business cycles in my model. They are responsible for 71 - 74% of the variance of output and 56 - 69% of the variance of consumption in the model. Financial shocks are important in explaining interest rate spreads and leverage ratios, but they account for less than 11% of the fluctuations in output. My results suggest that other factors, beyond credit market frictions on their own, are necessary to justify an important role for financial shocks in aggregate output fluctuations.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".