An Evaluation of the Forecasting Performance of Three Econometric Models for the Eurozone and the USA
Bibliographic record
Abstract
This paper compares the forecasting performance of three different econometric models for the Eurozone and the USA: a vector auto regression (VAR), a Bayesian vector auto regression (BVAR), and a structural vector error correction model (SVEC). The forecast evaluation is based on 19 vintages of real time data for output, inflation rates, interest rates, the exchange rate and the money stock from the fourth quarter of 2004 until the first quarter of 2010. The oil price is used as the only exogenous variable in the model. Imposing a stringent set of long-run assumptions on the econometric model results in less accurate forecasts. The difference is significant for several variables and forecast horizons. Reducing the comparison to data from the pre-financial crisis period reduces the size of forecast errors but does not change the overall picture.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".