Resampling methods in economics
Bibliographic record
Abstract
This dissertation is to investigate the application of bootstrapping methods in economics, for both theoretical and empirical analysis. The size and power of two J-type tests, a bootstrap and a pretest test, are compared for weakly correlated or nearly orthogonal non-nested regression models. Within the field of time series, the bootstrap technique is combined with the nonparametric methodology to estimate conditional quantiles for financial time series. Three newly developed bootstrap based methods (nonparametric wild bootstrap, block bootstrap and subsampling) are adopted, and the local linear nonparametric estimation is then used to estimate the conditional quantile. Moving block bootstrap is applied to generate confidence intervals for the conditional quantile estimation. The last part is to use semi-parametric models to explain university participation decisions of Canadian families with use of cross-sectional micro-data. The family's permanent income is estimated at the first stage and then included into the nonparametric part of the semi-parametric model. The wild bootstrap method is applied to generate confidence intervals of estimates to deal with the problem of introducing a generated regressor into regression models.
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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 | 0.030 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".