Forecast Accuracy Improvement: Evidence from U.S. Nonfarm Payroll Employment
Bibliographic record
Abstract
The timing of data release for a speciflc time period of observation is often spread over weeks. For instance o‐cial government statistics are often released at difierent times over the quarter or month and yet cover the same time period. This paper focuses on this separation of announcement timing or data release and the use of econometric real-time methods (what we call an updated vector autoregression forecast) to forecast data that has not yet been made available. In comparison to standard time series forecasting, we flnd that the updated multivariate time series forecasting will be more accurate with higher correlation coe‐cients among observation innovations. This updated forecast has a direct application in macro and flnancial series. One of the macro real variables, U.S. nonfarm payroll employment, is the flrst of its kind in the literature. We flnd that the relative e‐ciency gain by using the updated vector autoregression forecast is 16% in the one-step-ahead forecast and 7% in the two-step-ahead forecast, respectively, in comparison to the ordinary vector autoregression forecast. The results demonstrate the usefulness of updating multivariate forecast accurate measurements.
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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 | 0.008 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".