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, ...)A list with items depending on method
numeric vector, the regressand
numeric matrix, the regressors
NULL (default) or, when ols is used with method=6, a numeric vector containing the untransformed residuals
numeric value. The tolerance for detecting linear dependencies in the columns of the regressors, see the .lm.fit function
deprecated and ignored
an integer, 1 to 6, that determines the estimation method
NULL or a list with items that specifies the log-variance model to be estimated, see arx
further arguments (currently ignored)
Genaro Sucarrat, http://www.sucarrat.net/
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.
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.