THE USEFULNESS OF CONSUMER CONFIDENCE INDICES IN THE U.S.
Bibliographic record
Abstract
The purpose of this research is to assess the usefulness of consumer confidence indices in forecasting aggregate consumer spending in the U.S. The literature generally gives little intrinsic value to these indices. However, without formal modelling, some researchers (Garner (1991), and Throop (1992)) suggested that these indices could be helpful during periods of major economic or political shocks. Such periods are usually associated with high volatility of consumer confidence, suggesting that large swings in confidence could be useful indicators of consumption. Our work distinguishes itself from previous re-search in that we provide a rigorous assessment of this possibility by estimating a con-sumption function in which only large variations of confidence can affect spending. Our results show that economists and forecasters should be concerned with consumer confi-dence, especially in times of elevated economic or political uncertainty. *Thanks to J. Bailliu, D. Côté, Y. Desnoyers, J. Murray, J.-F. Perrault, L. Schembri, D. Tessier, and Bank of Canada semi-nar participants for several valuable comments and suggestions. The views in this paper are exclusively those of the authors and should not be attributed to the Bank of Canada. “In normal household sp times consum economic ind predict cons
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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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".