A guide to estimating the canonical income process in quasidifferences
Bibliographic record
Abstract
Abstract The canonical income process, including autoregressive, transitory, and fixed effect components, is routinely used in macro and labor economics. We provide a guide for its estimation using quasidifferences, cataloging biases in the estimated parameters for various $N$ , $T$ , initial conditions, and weighting schemes. Using Danish administrative data on male earnings, estimation in quasidifferences yields divergent estimates of the autoregressive parameter for different weighting schemes, which conforms to our simulation results when the variance of transitory shocks is higher than that of persistent shocks, true persistence is high, and the persistent component’s variance in the first sample year is nonzero. We further apply quasidifferences to the data from a calibrated lifecycle model and find significant biases in the persistence of shocks and their insurance. Estimation of the income process using quasidifferences is reliable only when the variance of persistent shocks is higher than that of transitory shocks and the moments are equally weighted.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.042 | 0.015 |
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".