Predictive Analysis of Fiscal Crises with Deep Learning Time Series Model
Bibliographic record
Abstract
Fiscal crisis can cause serious damage to the economy. Remarkably, there is limited study about when and how it occurs. With the social and economic data of more than 180 countries from 1970 to 2015, this paper constructs a fiscal crisis risk index system to explore the relationships between the crises and the indicators such as GDP growth rate, inflation rate, FDI, and foreign debt interests. Predictive analysis is performed based on the time series model of deep neural network to shed some light on policies and economic dynamics around the crises. We find that besides the inflation, fiscal crises in advanced economies are closely related to the net outflows of FDI and GDP p.c. while in developing countries the GDP growth rate and the net inflows of FDI are the key factors. Low-income developing countries are the heavy-hit targets with the net inflows of FDI, debt structure and interests as main contributors.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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".