Statistical Analysis of Minsky’s Financial Instability Hypothesis for the 1945–2023 Era
Bibliographic record
Abstract
Following the 2008 financial crisis, Hyman Minsky’s Financial Instability Hypothesis (FIH) emerged as a prominent financial theory to explain the occurrence of business cycles in the U.S. economy. There have been many theoretical, but few empirical studies dedicated to FIH. The current literature also lacks the statistical support to confirm the necessary conditions leading to financial instability and whether FIH concepts remains applicable in the post 1980s periods. This article presents a statistical methodology to analyze the financial debt ratios related to FIH for the 1945–2023 periods through the use of nonparametric statistical analyses of ordered alternatives and a binomial test for meta-analysis. The results indicated that the conditions leading to financial instability such as debt ratios did increase prior to the onset of a recession as prescribed by FIH during the 1945–1980s era. Furthermore, such conditions also repeated prior to some recessions occurred in the 2001–2023 periods. This study provides statistical support for Minsky’s FIH theory.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".