Informational Assumptions on Income Processes and Consumption in the Buffer Stock Model of Savings
Bibliographic record
Abstract
Idiosyncratic household income is typically assumed to consist of several components. While the total income is observed and is often modelled as an integrated moving average process, individual components are not observed directly. In the literature, econometricians typically assume that household income is the sum of a random walk permanent component and a transitory component, with uncorrelated permanent and transitory shocks. This characterization is not innocuous since households may have better information on individual income components than econometricians do. I show that, for the same reduced form model of income, different models for the income components lead to sizeably different estimates of the marginal propensity to consume (MPC) out of shocks to current and lagged income, and the volatility of consumption changes relative to income changes in data generated by an infinite horizon buffer stock model. I further suggest that the MPC out of shocks to current and lagged income estimated from empirical micro data should help identify parameters of individual components of the income process, including the correlation between transitory and permanent shocks. I use the method of simulated moments (MSM) and data from the Panel Study of Income Dynamics (PSID) and the Consumer Expenditure Survey (CEX) to estimate a structural life cycle model of consumption. I also jointly estimate the parameters governing the income process. I find statistically significant negative contemporaneous correlation between permanent and transitory shocks to income and reasonable, precisely estimated values for the time discount factor and the relative risk aversion parameter
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".