skedastic: Heteroskedasticity Diagnostics for Linear Regression Models

Implements numerous methods for detecting heteroskedasticity (sometimes called heteroscedasticity) in the classical linear regression model. These include the parametric and nonparametric tests of Goldfeld and Quandt (1965) <doi:10.1080/01621459.1965.10480811>, the test of Glejser (1969) <doi:10.1080/01621459.1969.10500976> as formulated by Mittelhammer, Judge and Miller (2000, ISBN: 0-521-62394-4), the BAMSET Test of Ramsey (1969) <doi:10.1111/j.2517-6161.1969.tb00796.x>, which uses the BLUS residuals derived by Theil (1965) <doi:10.1080/01621459.1965.10480851>, the test of Harvey (1976) <doi:10.2307/1913974>, the test of Breusch and Pagan (1979) <doi:10.2307/1911963> with and without the modification proposed by Koenker (1981) <doi:10.1016/0304-4076(81)90062-2>, the test of White (1980) <doi:10.2307/1912934>, the test and graphical Cook and Weisberg (1983) <doi:10.1093/biomet/70.1.1>, and the test of Li and Yao (2019) <doi:10.1016/j.ecosta.2018.01.001>. Homoskedasticity refers to the assumption of constant variance that is imposed on the model errors (disturbances); heteroskedasticity is the violation of this assumption.

Version: 0.1.0
Depends: R (≥ 3.5.0)
Imports: stats (≥ 3.5.0), utils (≥ 3.5.0), Rdpack (≥ 0.11.0), tibble (≥ 2.1.3), broom (≥ 0.5.3), magrittr (≥ 1.5.0), pracma (≥ 2.2.5), gmp (≥ 0.5.13), Rmpfr (≥ 0.7.2), matrixcalc (≥ 1.0.3), lubridate (≥ 1.7.4), lmtest (≥ 0.9.37), car (≥ 3.0.5), het.test (≥ 0.1), tseries (≥ 0.10.47)
Published: 2020-01-10
Author: Thomas Farrar ORCID iD [aut, cre], University of the Western Cape [cph]
Maintainer: Thomas Farrar <tjfarrar at>
License: MIT + file LICENSE
NeedsCompilation: no
Citation: skedastic citation info
Materials: README NEWS
In views: Econometrics
CRAN checks: skedastic results


Reference manual: skedastic.pdf
Package source: skedastic_0.1.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release: skedastic_0.1.0.tgz, r-oldrel: skedastic_0.1.0.tgz


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