Stability of Money Demand in the Russian Economy after the Global Financial Crisis of 2008-2009
Bibliographic record
Abstract
Existence of a favorable socioeconomic climate is now considered as a key factor of the long-term economic \ngrowth of a country. This is specifically true for the emerging economies in the modern global economic environment. For the Russian economy which has been facing economic sanctions from the part of Western countries, creation and maintaining of such climate is a crucial issue of survival. From this perspective, the efforts applied by the Bank of Russia and the Russian government were aimed at stabilization of the economy and creation of an attractive economic environment in the country. This paper studies the conditions under which this policy was carried out in 2011-2017. This study specifically focuses on one of the key aspects of success of such policy – stability of the money-demand function (MDF) in the Russian economy. The presence of such stability is studied using the cointegration analysis, and the type of relationship between national income and money demand is also identified. The findings of this research speak in favor of existence of a stable MDF in the Russian economy of that time. Thus, based on the Russian case, the paper’s contribution is empirical demonstration of the importance of MDF stability for success of monetary policy which is in line \nwith the extant literature.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".