MODELLING AND PREDICTING THE REAL MONEY DEMAND IN ROMANIA
Bibliographic record
Abstract
The main aim of this article is to model the quarterly real money demand in Romania and to make short-run forecasts for 2014:Q1-2015:Q1. A vector-autoregressive model (VAR(1)) was built for stationary data series of real money demand, real GDP and spread between active and pasive interest rate of the credit institutions over the period from 2000:Q1 to 2013:Q4. In the first period the variations in the double differentiated real money demand are exclusivly generated by the changes in this variable. The short-term forecasts based on this model indicated a slow variation in the rate of real money demand. For the first quarter of 2014 the comparison of the forecast with the actual value is made and an error of 0.94 percentage point was obtained. Starting with the second quarter of 2014, a slow decrease is anticpated for the rate of real money demand.
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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) | 0.001 | 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".