A guide to estimating the canonical income process in quasidifferences
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.004 |
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 it