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>.


Reference manual

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0.1.0 by Jonas Kurle, 12 days ago


Report a bug at https://github.com/jkurle/robust2sls/issues

Browse source code at https://github.com/cran/robust2sls

Authors: Jonas Kurle [aut, cre]

Documentation:   PDF Manual  

GPL-3 license

Imports AER, doRNG, foreach, pracma, stats

Suggests datasets, doFuture, doParallel, future, ggplot2, grDevices, knitr, MASS, parallel, rmarkdown, testthat, utils

See at CRAN