Replication Data for: Addressing Endogeneity Using a Two-Stage Copula Generated Regressor Approach
Bibliographic record
Abstract
We provide instructions, codes and datasets for replicating the paper by Yang, Qian and Xie (2024), "Addressing Endogeneity using a Two-stage Copula Generated Regressor Approach." First, we provide a R code to replicate the simulation studies: see simulation_code.R. Second, we provide a R code and a dataset from the public Dominick’s store-level scanner data for empirical illustration: see empirical_illustration_code.R and data_empirical_illustration.csv. [A guide on how to use the code to reproduce each study in the paper] 1. simulation_code.R: This is the R code for replication of simulation results. 2. empirical_illustration_code.R: This is the R code for replication of empirical illustration results in the main manuscript. 3. data_empirical_illustration.csv: This is the Dominick’s toothpaste sales data used for empirical analysis. [A list of the versions of R, packages, and computer specification used in the paper] R version: 4.2.1 (2022-06-23) Processor: MacBook Pro with M1 Pro chip Installed RAM: 32.0 GB To run the program, download all files and save them under the same folder. Then source the R files to run the programs. [Technical Help or Problem Report] We are expecting the R codes to be easy to use and to be working well. Please send any questions or report bugs to Fan Yang (E-mail: fanyang0903@gmail.com).
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.
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 | 0.029 | 0.230 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.255 | 0.136 |
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".