Real-Time Forecasting in Practice: The U.S. Treasury Staff's Real-Time GDP Forecast System
Bibliographic record
Abstract
This paper outlines a method for making effective use of monthly indicators to develop a current-quarter GDP forecast. Estimates and projections of real GDP growth are usually used to describe how the economy is doing. But estimates of GDP are only available quarterly, and the first GDP estimate for a quarter is released late in the month following the end of the quarter. The lack of a timely, comprehensive economic picture may mean that policymakers and business planners may be as much as four months behind in recognizing a significant slowdown or acceleration in the economy. This problem is especially important around business cycle peaks or troughs, where there may be some evidence that the economy is changing direction. There are many less-comprehensive, but higher-frequency data series about the economy, however. The chief difficulty with using the multiple indicators is that different indicators can give different signals, and there is no agreed-upon way for aggregating the statistics to give a single-valued answer. In this paper, we describe the approach we have adopted at the Treasury Department to use a broad variety of high-frequency incoming data to construct “realtime” estimates of quarterly real GDP growth. We draw on the recent work by Stock and Watson and others and describe the indicators, the techniques, and the recent performance of the system.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".