About the Method of Analysis of Economic Correlations by Differentiation of Spline Models
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Bibliographic record
Abstract
The article considers spline approximation as one of efficient methods of modeling economic dynamics. Spline approximation of economic dynamics allows carrying out qualitative and accurate transition from discrete values of a lattice function to a continuous model of a process, which allows calculating values of a studied index at any time point (interpolation). Spline representation improves the quality of economic dynamics modeling while saving the real values of the studied process at each time point. In this article, differentiation of spline models is used for analysis of the economic indexes growth rate. Correlations are detected and itemized by comparison of derivatives. The possibility of detecting "latent trends" is demonstrated by differentiation of spline models of the dynamics using the example of economic indexes of the oil and gas market of Russia. For example, in the first case, we consider spline models of the dynamics of export prices for oil and natural gas. Here, the correlation of the studied indexes is obvious and is detected by both calculation of the correlation ratio and visualization of the studied rows of dynamics with spline models. As an opposite example, we consider the dynamics of the volumes of oil and natural gas export. In this case, we gain the correlation ratio close to zero, which is to evidence absence of correlation. Modeling of the studied dynamics with cubic splines also does not detect any correlation between the dynamics of volumes of the oil and gas export. Our assumptions about "latent trends" are also confirmed by differentiation of spline models – the correlation between the change rate of the volumes of the oil and gas export is detected. Use of spline functions at economic dynamics modeling is determined with such positive properties of theirs as continuity, flexibility, differentiability, the property of minimal curve, etc.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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