Empirical applications of the local likelihood method
Bibliographic record
Abstract
In the econometrics literature, nonparametric estimation is relatively an emerging field with more research being directed towards developing more versatile and precise estimators. The main focus of my thesis is to implement the Local Maximum Likelihood (LML) to address empirical issues in finance and labour economics. The first chapter of the thesis compares the performance of several nonparametric estimators in dynamic Capital Asset Pricing Model (CAPM) framework. The simulations show that the choice of estimator matters in testing the validity of the CAPM. However, empirically the validity of the CAPM is rejected for all the competing estimators. The second chapter investigates the potential gains from estimating the treatment effect with the propensity score matching by relaxing the functional form assumptions of the parametric binary response models. The LML estimator is adapted to obtain nonparametric estimates of the propensity scores. Exhaustive simulation analysis show that the efficiency of the estimated treatment effect can be increased with nonparametric estimation. Furthermore, the empirical analysis of the experimental data shows that nonparametrically estimated propensity scores are more effective in eliminating selection bias. The final chapter estimates the private rate of return to training in Canada using an internal rate of return approach. The production and the cost functions of the firms are estimated by addressing the issues of endogeneity of inputs including human capital and unobservable firm heterogeneity with the system-GMM estimation. The findings of this chapter show that the formal training has a significant impact on productivity. The estimated rate of return to formal training is large and heterogeneous across the firms.
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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 | 0.019 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".