Bibliographic record
Abstract
Real Gross Domestic Product is usually computed at quarterly intervals, which makes it uncomfortable to introduce into different types of macroeconomic models, that usually make use of higher frequency data (monthly, weekly) such as inflation, interest and unemployment. Analysts and decision makers may also want to evaluate a close evolution of the aggregated national output, especially during the economic slowdown periods, just as the one which has spread almost worldwide. Moreover, it is well known that the capability of correctly identifying the short-term pattern of an economic phenomenon is directly linked to the frequency of available observations.For economic studies using quarterly data, a low number of observations can cause serious flaws in the quality of quantitative analysis, without even considering the situations when many degrees of freedom are used up in the estimation, this way drastically reducing its power. Another limitation stands in the short sample size for developing countries as result of deep structural changes that occurred in the past couple of decades. One way around this problem is to estimate higher frequency series, using information from the low frequency GDP series and some related series. The paper illustrates and evaluates a Kalman filtering method for forecasting the seasonally adjusted Romanian real GDP at monthly intervals. However the present mixed-frequency method produces monthly GDP forecasts for the first two months of a quarter ahead which are more accurate than one-quarter-ahead GDP forecast based on purely-quarterly data. The purpose of this paper is to achieve this temporarily desegregation for Romanian dates and show few possible applications of these results, such as testing an eventual existence of Taylor Rule based monetary policy among the Central Banks of five economies.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".