Stochastic Dynamic Revealed Preferences for Non-Linear Budget Constraints
Bibliographic record
Abstract
This paper proposes a nonparametric framework to test and estimate the classic exponential discounting, time separable consumer model in an environment with noisy data caused by the presence of measurement error. Our methodology extends the dynamic revealed preferences framework to make it applicable to survey data where measurement error causes a loss of distributional information that prevents us from applying the revealed preferences tools at the individual level. We combine the strengths of the revealed preferences approach to avoid making arbitrary parametric assumptions on the shape of the utility function, and we use a latent variable integration technique proposed in Schennach (2014) to deal with heterogeneity and measurement error without making strong distributional assumptions. We establish the asymptotic behavior of the estimator and prove the validity of inference using subsampling. Monte Carlo simulations show that an otherwise time-consistent consumer may often be mistakenly taken to be inconsistent in the presence of measurement error by the standard deterministic revealed preferences tests; our proposed methodology does not reject the exponential discounting model in such cases. We find support for exponential discounting behavior in a consumption panel survey for single-individual households, while rejecting the model for the case of couples. The second result is specially interesting, as we establish theoretically, that under measurement error, a version of the collective household exponential discounter model with intra-household preference heterogeneity presented in Adams et al. (2014) (that rationalizes the dataset we use) has the same implications that the standard exponential discounting model.
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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 | 0.016 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".