Asset Pricing with Idiosyncratic Consumption Risk and Limited Participation
Bibliographic record
Abstract
A growing body of literature suggests limited asset market participation as a plausible explanation of the empirical failure of the standard consumption capital asset pricing model (CCAPM). Correct identification of capital markets investors is, however, often impossible due to imperfection of available information on assetholding status. As a plausible solution to the problem of sample classification when available information is an imperfect sample separation indicator, we propose the CCAPM in which the pricing kernel is calculated as the weighted average of individual households¡¯ marginal rate of substitution, with the weights being the probabilities of holding the asset in question. The asset holding probabilities are conditional on available sample separation information and estimated from a binary response model as a function of demographic and family characteristics of consumers simultaneously with the parameters of Euler equations. The CCAPM with probability-weighted agents is less susceptible to sample misclassification compared to when available imperfect information on asset holding status is used to separate assetholders from nonassetholders. Using data from the U.S. Consumer Expenditure Survey (CEX), we find that, in contrast to when the reported in the CEX financial information is regarded as a perfect sample separation indicator, the model with probability-weighted agents is not rejected statistically both under conventional normal and weak-identification asymptotics and yields precise and economically realistic estimates of the coefficient of relative risk aversion (RRA). The hypothesis that the households¡¯ market participation behavior exhibits considerable persistence is not rejected statistically. Empirical evidence is that the decision to own assets is likely to be endogenous with respect to the consumption and savings decisions and that allowing for this fact is important for estimating risk aversion.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".