Analytical Approaches to Macroeconomic Forecasting: A Study of Profits through Machine Learning and Deep Learning
Why this work is in the frame
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Bibliographic record
Abstract
With a basis on the analytical framework of Levy and Kalecki's Corporate Profits Equation, this MQP uses Machine Learning and Deep Learning to provide a forecast for aggregate corporate profits in the United States. The tool used to deliver this forecast was the RapidMiner Software and the data source was the Federal Reserve Bank of St. Louis. The independent variable was Aggregate Profits for the following quarter and the dependent variables were Investment, Dividend, Household Saving, Net Government Saving, ROW Saving and the Statistical Discrepancy. Making use of these predictions and relying on economic theory, this paper explores the repercussions of assumptions made through the Cambridge Controversies until today, regarding the relationship between the working class and the elite.
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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) | 0.000 | 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 it