The Estimation of Various Shocks Influence on the Dynamics of Russian Macroeconomic Indicators in 2014–2018
Bibliographic record
Abstract
stract In this paper, we analyze the influence of various macroeconomic shocks caused by anti-Russian sanctions and a sharp decrease in the hydrocarbon prices in the middle of the last decade. We estimate the total loss in the economy and identify the shocks that provoked the decline in GDP and the increase in inflation from 2014 to 2018 using the DSGE approach and the obtained historical decompositions. According to the calculations, the inflation growth from 2014 to 2015 can be interpreted as the sum of the adverse effects of the change in household preferences, and the shock in oil prices. The observed GDP decline from the second quarter of 2014 to the third quarter of 2015 is explained by the synergistic effect of monetary policy shocks and the sharp drop in oil prices. According to our calculations, the total loss in the economy due to the described shocks in real terms is equal to 6.4 trillion rubles in 2011 prices.
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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 | 0.001 | 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 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".