Macroeconomic Challenges in the Euro Area and the Acceding Countries
Bibliographic record
Abstract
The conduct of effective economic policy faces a multiplicity of macroeconomic challenges, which requires a wide scope of theoretical and empirical analyses. With a focus on the European Union, this doctoral dissertation consists of two parts which make empirical and methodological contributions to the literature on forecasting real economic activity and on the analysis of business cycles in a boom-bust framework in the light of the EMU enlargement. In the first part, we tackle the problem of publication lags and analyse the role of the information flow in computing short-term forecasts up to one quarter ahead for the euro area GDP and its main components. A huge dataset of monthly indicators is used to estimate simple bridge equations. The individual forecasts are then pooled, using different weighting schemes. To take into consideration the release calendar of each indicator, six forecasts are compiled successively during the quarter. We find that the sequencing of information determines the weight allocated to each block of indicators, especially when the first month of hard data becomes available. This conclusion extends the findings of the recent literature. Moreover, when combining forecasts, two weighting schemes are found to outperform the equal weighting scheme in almost all cases.\nIn the second part, we focus on the potential accession of the new EU Member States in Central and Eastern Europe to the euro area. In contrast to the discussion of Optimum Currency Areas, we follow a non-standard approach for the discussion on abandonment of national currencies the boom-bust theory. We analyse whether evidence for boom-bust cycles is given and draw conclusions whether these countries should join the EMU in the near future. Using a broad range of data sets and empirical methods we document credit market imperfections, comprising asymmetric financing opportunities across sectors, excess foreign currency liabilities and contract enforceability problems both at macro and micro level. Furthermore, we depart from the standard analysis of comovements of business cycles among countries and rather consider long-run and short-run comovements across sectors. While the results differ across countries, we find evidence for credit market imperfections in Central and Eastern Europe and different sectoral reactions to shocks. This gives favour for the assessment of the potential euro accession using this supplementary, non-standard approach.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 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".