Parametric and nonparametric income distribution estimators in CGE micro-simulation modeling
Bibliographic record
Abstract
We consider the issue of income distribution modeling in the context of poverty analysis impact based on computable general equilibrium micro-simulation models. The empirical distribution function (EDF) is by far the most commonly used estimator in practice. It is, however, not the only available consistent estimator and there may be situations in which a different estimator would be able to provide more accurate results. An alternative is to use a smooth estimator of the population income distribution. Two types of such estimators are available: parametric and nonparametric ones. In the first case, one has to chose a particular parametric form for the distribution function and estimates its parameters. The main drawback is the difficulty associated with the selection of the functional form. The nonparametric approach sidesteps this functional form issue by using kernel density estimators that only impose mild restrictions on the distribution function. This is obviously an important advantage, but its cost is that the accuracy of these estimators typically depends to a large extent on the bandwidth used in the kernel function. Another advantage is that it nests the EDF as a special case. We propose to extend the work of Boccanfuso et al. (2008) in two ways. First, we consider a larger set of parametric functions, including the 5 parameter generalized beta distribution and some of its special cases. Second, we use non-parametric kernel estimators and study their accuracy under different bandwidth selection schemes. Lastly, we provide Monte Carlo comparisons of the accuracy of these methods with the widely used EDF.
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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 | 0.013 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".