hccme

Computes an estimate of the $n\times n$ covariance matrix $\Omega$
 (assumed to be diagonal) of the random error vector of a linear
 regression model, using a specified method

Implements numerous methods for testing for, modelling, and
correcting for heteroskedasticity in the classical linear regression
model. The most novel contribution of the
package is found in the functions that implement the as-yet-unpublished
auxiliary linear variance models and auxiliary nonlinear variance
models that are designed to estimate error variances in a heteroskedastic
linear regression model. These models follow principles of statistical
learning described in Hastie (2009) <doi:10.1007/978-0-387-21606-5>.
The nonlinear version of the model is estimated using quasi-likelihood
methods as described in Seber and Wild (2003, ISBN: 0-471-47135-6).
Bootstrap methods for approximate confidence intervals for error variances
are implemented as described in Efron and Tibshirani
(1993, ISBN: 978-1-4899-4541-9), including also the expansion technique
described in Hesterberg (2014) <doi:10.1080/00031305.2015.1089789>. The
wild bootstrap employed here follows the description in Davidson and
Flachaire (2008) <doi:10.1016/j.jeconom.2008.08.003>. Tuning of
hyper-parameters makes use of a golden section search function that is
modelled after the MATLAB function of Zarnowiec (2022)
<https://www.mathworks.com/matlabcentral/fileexchange/25919-golden-section-method-algorithm>.
A methodological description of the algorithm can be found in Fox (2021,
ISBN: 978-1-003-00957-3).
There are 25 different functions that implement hypothesis tests for
heteroskedasticity. These include a test based on Anscombe (1961)
<https://projecteuclid.org/euclid.bsmsp/1200512155>, Ramsey's (1969)
BAMSET Test <doi:10.1111/j.2517-6161.1969.tb00796.x>, the tests of Bickel
(1978) <doi:10.1214/aos/1176344124>, 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>, Carapeto and
Holt (2003) <doi:10.1080/0266476022000018475>, Cook and Weisberg (1983)
<doi:10.1093/biomet/70.1.1> (including their graphical methods), Diblasi
and Bowman (1997) <doi:10.1016/S0167-7152(96)00115-0>, Dufour, Khalaf,
Bernard, and Genest (2004) <doi:10.1016/j.jeconom.2003.10.024>, Evans and
King (1985) <doi:10.1016/0304-4076(85)90085-5> and Evans and King (1988)
<doi:10.1016/0304-4076(88)90006-1>, Glejser (1969)
<doi:10.1080/01621459.1969.10500976> as formulated by
Mittelhammer, Judge and Miller (2000, ISBN: 0-521-62394-4), Godfrey and
Orme (1999) <doi:10.1080/07474939908800438>, Goldfeld and Quandt
(1965) <doi:10.1080/01621459.1965.10480811>, Harrison and McCabe (1979)
<doi:10.1080/01621459.1979.10482544>, Harvey (1976) <doi:10.2307/1913974>,
Honda (1989) <doi:10.1111/j.2517-6161.1989.tb01749.x>, Horn (1981)
<doi:10.1080/03610928108828074>, Li and Yao (2019)
<doi:10.1016/j.ecosta.2018.01.001> with and without the modification of
Bai, Pan, and Yin (2016) <doi:10.1007/s11749-017-0575-x>, Rackauskas and
Zuokas (2007) <doi:10.1007/s10986-007-0018-6>, Simonoff and Tsai (1994)
<doi:10.2307/2986026> with and without the modification of Ferrari,
Cysneiros, and Cribari-Neto (2004) <doi:10.1016/S0378-3758(03)00210-6>,
Szroeter (1978) <doi:10.2307/1913831>, Verbyla (1993)
<doi:10.1111/j.2517-6161.1993.tb01918.x>, White (1980)
<doi:10.2307/1912934>, Wilcox and Keselman (2006)
<doi:10.1080/10629360500107923>, Yuce (2008)
<https://dergipark.org.tr/en/pub/iuekois/issue/8989/112070>, and Zhou,
Song, and Thompson (2015) <doi:10.1002/cjs.11252>. Besides these
heteroskedasticity tests, there are supporting functions that compute the
BLUS residuals of Theil (1965) <doi:10.1080/01621459.1965.10480851>, the
conditional two-sided p-values of Kulinskaya (2008) <arXiv:0810.2124v1>,
and probabilities for the nonparametric trend statistic of Lehmann (1975,
ISBN: 0-816-24996-1). For handling heteroskedasticity, in addition to the
new auxiliary variance model methods, there is a function
to implement various existing Heteroskedasticity-Consistent Covariance
Matrix Estimators from the literature, such as those of White (1980)
<doi:10.2307/1912934>, MacKinnon and White (1985)
<doi:10.1016/0304-4076(85)90158-7>, Cribari-Neto (2004)
<doi:10.1016/S0167-9473(02)00366-3>, Cribari-Neto et al. (2007)
<doi:10.1080/03610920601126589>, Cribari-Neto and da Silva (2011)
<doi:10.1007/s10182-010-0141-2>, Aftab and Chang (2016)
<doi:10.18187/pjsor.v12i2.983>, and Li et al. (2017)
<doi:10.1080/00949655.2016.1198906>.

Thomas Farrar

skedastic

Handling Heteroskedasticity in the Linear Regression Model

University of the Western Cape 

hccme function

<dl><dt>mainlm</dt>
<dd>Either an object of <code><a href="/link/class?package=skedastic&version=2.0.2" data-mini-rdoc="skedastic::class">class</a></code> <code>"lm"</code>
(e.g., generated by <code><a href="/link/lm?package=skedastic&version=2.0.2" data-mini-rdoc="skedastic::lm">lm</a></code>), or
a list of two objects: a response vector and a design matrix. The objects
are assumed to be in that order, unless they are given the names
<code>"X"</code> and <code>"y"</code> to distinguish them. The design matrix passed
in a list must begin with a column of ones if an intercept is to be
included in the linear model. The design matrix passed in a list should
not contain factors, as all columns are treated 'as is'. For tests that
use ordinary least squares residuals, one can also pass a vector of
residuals in the list, which should either be the third object or be
named <code>"e"</code>.</dd>
<dt>hcnum</dt>
<dd>A character corresponding to a subscript in the name of an
HCCME according to the usual nomenclature $\mathrm{HC\#}$.
Possible values are:<ul>
<li>"3", the default, corresponding to HC3
MacKinnon85skedastic</li>
<li>"0", corresponding to HC0 White80skedastic</li>
<li>"1", corresponding to HC1 MacKinnon85skedastic</li>
<li>"2", corresponding to HC1 MacKinnon85skedastic</li>
<li>"4", corresponding to HC4 Cribari04skedastic</li>
<li>"5", corresponding to HC5 Cribari07skedastic</li>
<li>"6", corresponding to HC6 Aftab16skedastic</li>
<li>"7", corresponding to HC7 Aftab18skedastic</li>
<li>"4m", corresponding to HC4m Cribari11skedastic</li>
<li>"5m", corresponding to HC5m Li17skedastic</li>
<li>"const", corresponding to the homoskedastic estimator,
 $(n-p)^{-1}\displaystyle\sum_{i=1}^{n}e_i^2$</li>
</ul></dd>
<dt>sandwich</dt>
<dd>A logical, defaulting to <code>FALSE</code>, indicating
whether or not the sandwich estimator
$$\mathrm{Cov}{\hat{\beta}}=(X'X)^{-1}X'\hat{\Omega}X(X'X)^{-1}$$
should be returned instead of $\mathrm{Cov}(\epsilon)=\hat{\Omega}$</dd>
<dt>as_matrix</dt>
<dd>A logical, defaulting to <code>TRUE</code>, indicating whether
a covariance matrix estimate should be returned rather
than a vector of variance estimates</dd></dl>

Arguments

Heteroskedasticity-Consistent Covariance Matrix Estimators for
   Linear Regression Models — hccme

<dl>

<dt>mainlm</dt>
<dd>Either an object of <code><a href='https://rdrr.io/r/base/class.html'>class</a></code> <code>"lm"</code>
(e.g., generated by <code><a href='https://rdrr.io/r/stats/lm.html'>lm</a></code>), or
a list of two objects: a response vector and a design matrix. The objects
are assumed to be in that order, unless they are given the names
<code>"X"</code> and <code>"y"</code> to distinguish them. The design matrix passed
in a list must begin with a column of ones if an intercept is to be
included in the linear model. The design matrix passed in a list should
not contain factors, as all columns are treated 'as is'. For tests that
use ordinary least squares residuals, one can also pass a vector of
residuals in the list, which should either be the third object or be
named <code>"e"</code>.</dd>


<dt>hcnum</dt>
<dd>A character corresponding to a subscript in the name of an
HCCME according to the usual nomenclature $\mathrm{HC\#}$.
Possible values are:<ul>
<li>"3", the default, corresponding to HC3
MacKinnon85skedastic</li>
<li>"0", corresponding to HC0 White80skedastic</li>
<li>"1", corresponding to HC1 MacKinnon85skedastic</li>
<li>"2", corresponding to HC1 MacKinnon85skedastic</li>
<li>"4", corresponding to HC4 Cribari04skedastic</li>
<li>"5", corresponding to HC5 Cribari07skedastic</li>
<li>"6", corresponding to HC6 Aftab16skedastic</li>
<li>"7", corresponding to HC7 Aftab18skedastic</li>
<li>"4m", corresponding to HC4m Cribari11skedastic</li>
<li>"5m", corresponding to HC5m Li17skedastic</li>
<li>"const", corresponding to the homoskedastic estimator,
 $(n-p)^{-1}\displaystyle\sum_{i=1}^{n}e_i^2$</li>
</ul></dd>


<dt>sandwich</dt>
<dd>A logical, defaulting to <code>FALSE</code>, indicating
whether or not the sandwich estimator
$$\mathrm{Cov}{\hat{\beta}}=(X'X)^{-1}X'\hat{\Omega}X(X'X)^{-1}$$
should be returned instead of $\mathrm{Cov}(\epsilon)=\hat{\Omega}$</dd>


<dt>as_matrix</dt>
<dd>A logical, defaulting to <code>TRUE</code>, indicating whether
a covariance matrix estimate should be returned rather
than a vector of variance estimates</dd>

</dl>

hccme: Heteroskedasticity-Consistent Covariance Matrix Estimators for Linear Regression Models

Description

Usage

Value

Arguments

References

See Also

Examples