Heteroskedasticity Diagnostics for Linear Regression Models

Implements numerous methods for detecting heteroskedasticity (sometimes called heteroscedasticity) in the classical linear regression model. These include a test based on Anscombe (1961) < https://projecteuclid.org/ euclid.bsmsp/1200512155>, Ramsey's (1969) BAMSET Test , the tests of Bickel (1978) , Breusch and Pagan (1979) with and without the modification proposed by Koenker (1981) , Carapeto and Holt (2003) , Cook and Weisberg (1983) (including their graphical methods), Diblasi and Bowman (1997) , Dufour, Khalaf, Bernard, and Genest (2004) , Evans and King (1985) and Evans and King (1988) , Glejser (1969) as formulated by Mittelhammer, Judge and Miller (2000, ISBN: 0-521-62394-4), Godfrey and Orme (1999) , Goldfeld and Quandt (1965) , Harvey (1976) , Honda (1989) , Horn (1981) , Li and Yao (2019) with and without the modification of Bai, Pan, and Yin (2016) , Rackauskas and Zuokas (2007) , Simonoff and Tsai (1994) with and without the modification of Ferrari, Cysneiros, and Cribari-Neto (2004) , Szroeter (1978) , Verbyla (1993) , White (1980) , Wilcox and Keselman (2006) , and Zhou, Song, and Thompson (2015) . Besides these heteroskedasticity tests, there are supporting functions that compute the BLUS residuals of Theil (1965) , the conditional two-sided p-values of Kulinskaya (2008) , and probabilities for the nonparametric trend statistic of Lehmann (1975, ISBN: 0-816-24996-1). Homoskedasticity refers to the assumption of constant variance that is imposed on the model errors (disturbances); heteroskedasticity is the violation of this assumption.