An ordered probit model of an early warning system for predicting financial crisis in India
Bibliographic record
Abstract
The Indian economy is facing new challenges of maintaining financial stability with greater integration in terms of trade and finance with global economy. In the face of present global financial crisis which was triggered by liquidity shortfall in the overseas banking system, there is a need for developing an early warning system (EWS) incorporating global and domestic macroeconomic indicators for monitoring and maintaining financial stability in an economy. The financial sector in India is still dominated by banking sector and they hold the key to the stability of the entire financial system in the country. With this background, an attempt has been made to predict the financial crisis (fragile situation) in India using ordered probit model. In this paper, using index method of recognizing exact month during which the banking sector has experience crisis, we constructed monthly banking sector fragility index (BSF) of India and developed the ordered probit model for predicting the banking crisis using macroeconomic indicators. The banking fragility index of India identifies nineteen phases of medium fragility and eight phases of high fragility during the studied sampled period, March 2000 to November 2009. The model could classify about 94 percent of different state of the crisis viz., no distress, medium and high fragility, in India.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".