Forecasts of US housing starts: assessing the usefulness of nowcast data
Bibliographic record
Abstract
This study focuses on the current-quarter and one – through four-quarter-ahead Federal Reserve forecasts of housing starts. The aim is to assess the usefulness of nowcast data (measured by the current-quarter forecasts) for predicting housing starts one through four quarters ahead. Specifically, we use the nowcast data to generate the one – through four-quarter-ahead nowcast-based forecasts. For 1985–2016, the Federal Reserve and nowcast-based forecasts (while outperforming the naïve forecasts) contain useful and distinct predictive information. Combining these forecasts yields reductions in forecast errors that are larger at longer horizons. In addition, the Federal Reserve (nowcast-based) forecasts imply symmetric (asymmetric) loss. The nowcast-based forecasts, in particular, are of value to a user who assigns high (low) cost to incorrect downward (upward) moves and, thus, offer useful information for policymaking, when downward moves in housing starts are considered as early-warning signs of overall economic downturns.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".