Chain-linking in Austrian quarterly national accounts and the business cycle
Bibliographic record
Abstract
In 2005, European Union member countries began to calculate national account volume estimates using prices from the previous year, rather than from a fixed base year. For quarterly national accounts, the average of the total previous year – and not of the previous quarter – began to serve as the price basis. This allows for the use of a Laspeyres-type quantity index. In order to obtain a time series of absolute values of volume estimates, it is necessary to chain-link growth rates. This is straightforward when calculating annual figures, but when calculating quarterly figures, EU countries can choose from one of three methods. This results in different outputs, time-series properties and, possibly, price-adjusted quarterly national account figures. The current study demonstrates the different results obtained using the three methods, when applied to Austrian quarterly GDP data. I observe the consequences of consecutive time-series-based processing, such as seasonal adjustment and business cycle analysis. Although dating turning points are rather robust using all three methods, seasonal and workday adjustment and detection of outliers based on time-series modelling can be negatively affected, as can business cycle dating.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".