Role of Advertising Expenditure as an Influential Non-traditional Regressor in Russia’s Money Demand Specification
Bibliographic record
Abstract
The term advertising refers to the strategy that affects consumer behaviour and induces higher household final consumption expenditure (HFCE), which is associated with incredibly demanding for money upon transaction usages and a reduction in search costs. The model of money demand that is stable, reliable and well defined is crucial for central banks in formulating their monetary policy to minimise the gap between the supply and demand of money. Hence, this paper examines the influence of expenditure in advertising (ADEX) towards the level of demand for money among the households in Russia. An approach known as Autoregressive distributed lag (ARDL) is opted to model the money demand function (MDF) of Russia and nine years of quarterly data from 2008 to 2016 have been used in the estimation. Empirical findings reveal that ADEX not only positively influences the Russian’s money demand in long term, its MDF also becomes more superior when the ADEX has been added. As such, this study suggests that ADEX can be taken into account as a non-traditional explanatory variable in the formulation of a stable and well-specified MDF for the case in Russia.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".