A Multivariate Filter to Estimate Potential Output and NAIRU for the Maltese Economy
Bibliographic record
Abstract
This paper applies a multivariate filter on a small macroeconomic model to derive estimates of Malta’s potential output growth, the output gap and NAIRU. The unobservable variables are derived from a system that incorporates long-standing relationships in economic theory, such as the Phillips Curve and Okun’s Law, while also allowing for hysteresis effects. Given the structural changes in the Maltese economy, with a shift over the past decade from traditional industries such as manufacturing towards higher-value added and export-oriented services, the model replaces a common variable used in the literature, capacity utilization in manufacturing, with two foreign variables, demand and imported inflation. The inclusion of foreign variables is important since Malta is one of the most open economies in the world with a high degree of import content. The model is also able to account for the high degree of volatility manifested in the time series of very small open economies. The estimates from the multivariate filter are compared with those derived from alternative approaches. Despite the negative impact from the financial crisis of 2009, by 2014 potential output growth had already surpassed the pre-crisis growth rates. The crisis had no permanent impact on NAIRU. This performance is clearly at odds with that of other European economies and bodes well for Malta’s convergence process.
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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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".