View source: R/gets-base-source.R
ols | R Documentation |
OLS estimation with the QR decomposition and, for some options, computation of variance-covariance matrices
ols(y, x, untransformed.residuals=NULL, tol=1e-07, LAPACK=FALSE, method=3,
variance.spec=NULL, ...)
y |
numeric vector, the regressand |
x |
numeric matrix, the regressors |
untransformed.residuals |
|
tol |
numeric value. The tolerance for detecting linear dependencies in the columns of the regressors, see the |
LAPACK |
deprecated and ignored |
method |
an integer, 1 to 6, that determines the estimation method |
variance.spec |
|
... |
further arguments (currently ignored) |
method = 1
or method = 2
only returns the OLS coefficient estimates together with the QR- information, the former being slightly faster. method=3
returns, in addition, the ordinary variance-covariance matrix of the OLS estimator. method=4
returns the White (1980) heteroscedasticity robust variance-covariance matrix in addition to the information returned by method=3
, whereas method=5
does the same except that the variance-covariance matrix now is that of Newey and West (1987). method=6
undertakes OLS estimation of a log-variance model, see Pretis, Reade and Sucarrat (2018, Section 4). Alternatively, for method
1 to 5, a log-variance model is also estimated if variance.spec
is not NULL
.
A list with items depending on method
Genaro Sucarrat, http://www.sucarrat.net/
W. Newey and K. West (1987): 'A Simple Positive Semi-Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix', Econometrica 55, pp. 703-708.
F. Pretis, J. Reade and G. Sucarrat (2018): 'Automated General-to-Specific (GETS) Regression Modeling and Indicator Saturation for Outliers and Structural Breaks', Journal of Statistical Software 86, Issue 3, pp. 1-44, DOI: https://doi.org/10.18637/jss.v086.i03
H. White (1980): 'A Heteroskedasticity-Consistent Covariance Matrix and a Direct Test for Heteroskedasticity', Econometrica 48, pp. 817-838.
.lm.fit
, qr
, solve.qr
, arx
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