robust2sls: Outlier Robust Two-Stage Least Squares Inference and Testing
An implementation of easy tools for outlier robust inference in two-stage least squares (2SLS) models. The user specifies a reference distribution against which observations are classified as outliers or not. After removing the outliers, adjusted standard errors are automatically provided. Furthermore, several statistical tests for the false outlier detection rate can be calculated. The outlier removing algorithm can be iterated a fixed number of times or until the procedure converges. The algorithms and robust inference are described in more detail in Jiao (2019) <https://drive.google.com/file/d/1qPxDJnLlzLqdk94X9wwVASptf1MPpI2w/view>.
| Version: | 0.2.2 |
| Depends: | R (≥ 2.10) |
| Imports: | exactci, foreach, ivreg, MASS, mathjaxr, pracma, stats |
| Suggests: | covr, datasets, doFuture, doParallel, doRNG, future, ggplot2, grDevices, ivgets, knitr, parallel, rmarkdown, testthat, utils |
| Published: | 2023-01-11 |
| DOI: | 10.32614/CRAN.package.robust2sls |
| Author: | Jonas Kurle  [aut,
cre] |
| Maintainer: | Jonas Kurle <mail at jonaskurle.com> |
| BugReports: | https://github.com/jkurle/robust2sls/issues |
| License: | GPL-3 |
| URL: | https://github.com/jkurle/robust2sls |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | robust2sls results |
Documentation:
| Reference manual: | robust2sls.pdf |
| Vignettes: |
Monte Carlo Simulations Outlier Testing Introduction to the robust2sls Package |
Downloads:
| Package source: | robust2sls_0.2.2.tar.gz |
| Windows binaries: | r-devel: robust2sls_0.2.2.zip, r-release: robust2sls_0.2.2.zip, r-oldrel: robust2sls_0.2.2.zip |
| macOS binaries: | r-release (arm64): robust2sls_0.2.2.tgz, r-oldrel (arm64): robust2sls_0.2.2.tgz, r-release (x86_64): robust2sls_0.2.2.tgz, r-oldrel (x86_64): robust2sls_0.2.2.tgz |
| Old sources: | robust2sls archive |
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