Estimating sentiment and risk in a consumption model: a factor analysis approach
Bibliographic record
Abstract
Abstract This empirical paper deals with the impacts of sentiment about the future, short-run risk, and long-run risk in a dynamic economic model of optimal consumption decisions with Schroder and Skiadas [(1999) Journal of Economic Theory 89, 68–126.] continuous-time stochastic recursive preferences. The empirical strategy combines both a latent factor method and a democratic orthogonalization technique. The latent factor method is applied to a large database of macroeconomic indicators, and a democratic orthogonalization technique is used to separate the relative importance of sentiment about the future and long-run risk channels in shaping optimal consumption decisions. The empirical results suggest that consumers with recursive preferences are not indifferent to long-run uncertainty shocks to future consumption prospects. Endogenous consumption variations are driven by a multicomponent mechanism, where on average, the sentiment component accounts for 15.33%, the short-run risk accounts for 16.89%, and the long-run risk pertains to 34.51%.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".