Stochastic model of economic cycles and its econometric application
Bibliographic record
Abstract
In this paper, I demonstrate the adequacy of economic systems to the basic provisions of synergetics, which makes the latter eligible for macroeconomic analysis. In justifying this statement, a synergetic approach to the development of a model of economic cycles was considered. The novelty of this model was related to the probabilistic description of the investment function and the perception of the economic system as a material object with certain properties. According to the model, the income oscillations are induced by both exogenous (investment fluctuations) and endogenous (system elasticity) causes. The cycle's amplitudes correlate with the intensity of investment fluctuations as well as the efficiency of the economic system. The duration of the economic cycle is determined by the inclusive wealth of the system and its dynamic factor, which characterizes the ability of the system to withstand investment fluctuations and eliminates their consequences. Thus, the economic cycle is interpreted as the "natural noise" accompanying the functioning of market economics. The proposed model creates a mathematical basis for the numerical analysis of empirical economic data. An example of possible econometric applications of the model was considered using the current cyclic contraction in US incomes.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".