Estimating Potential Output in the Republic of Croatia Using a Multivariate Filter
Bibliographic record
Abstract
This paper estimates potential output in the Republic of Croatia for the period between the first quarter of 2000 and the fourth quarter of 2010, using a combination of a multivariate Kalman filter and the regularised maximum likelihood method. For the estimation of potential output a dynamic macroeconomic model was developed, similar to that used in Benes et al. (2010), in which core inflation is the key determinant of potential output and the output gap. This is why potential output, as defined herein, can be construed as the level of output that can be sustained in the long run without creating either upward or downward pressures on core inflation. Apart from the aforementioned core inflation, the model includes some other relevant economic series, such as the unemployment rate, retail trade, industrial production index and current account deficit, which, if ignored, as in the case of univariate filters, can result in a potential output estimation bias. The estimation results show that output was below its potential level until the second quarter of 2002, after which it remained above the potential level for almost seven years. In the second quarter of 2009, however, output sank below its potential level, where it remained until the end of the reference period. During the last observed period, both actual and potential output levels declined.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".